Endpoints: 28,729MCP servers: 18,413Payout addresses: 2,071Paid calls: 1,533Letters: 13Defects: 1,322counted 3 min ago
teppi

Server definition

Hash
sha256:cc0b73694c30b7a97a6dc327cd45b6df3c7cd0caba044f33c3c01436c1697a8c
What it is
What a remote MCP server returned when asked what it offers: 279 tools

The blob, as servednamed by its sha256

{ "instructions": null, "tools": [ { "description": "Architecte ABM — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Gapup Hub — ABM 20 comptes nommés · Budget €120k · Tier 1×5 + Tier 2×15 · Playbooks 3 niveaux. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "abmBudgetEur": { "minimum": 0, "type": "number" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 100, "type": "string" }, "sector": { "maxLength": 100, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "currentChannels": { "items": { "maxLength": 100, "type": "string" }, "maxItems": 8, "type": "array" }, "icpCriteria": { "items": { "maxLength": 200, "type": "string" }, "maxItems": 6, "minItems": 2, "type": "array" }, "product": { "properties": { "avgDealValueEur": { "minimum": 0, "type": "number" }, "name": { "maxLength": 100, "type": "string" } }, "required": [ "name" ], "type": "object" }, "salesTeam": { "properties": { "aeCount": { "minimum": 0, "type": "integer" }, "csCount": { "minimum": 0, "type": "integer" }, "sdrCount": { "minimum": 0, "type": "integer" } }, "required": [ "aeCount" ], "type": "object" }, "targetAccounts": { "items": { "properties": { "currentStage": { "enum": [ "unaware", "aware", "engaged", "opportunity", "customer" ], "type": "string" }, "estimatedRevEur": { "minimum": 0, "type": "number" }, "industry": { "maxLength": 100, "type": "string" }, "keyContacts": { "items": { "maxLength": 100, "type": "string" }, "maxItems": 5, "type": "array" }, "name": { "maxLength": 100, "type": "string" }, "notes": { "maxLength": 300, "type": "string" } }, "required": [ "name" ], "type": "object" }, "maxItems": 50, "minItems": 5, "type": "array" } }, "required": [ "company", "product", "targetAccounts", "icpCriteria" ], "type": "object" }, "name": "abm_architect", "outputSchema": null }, { "description": "As a CMO, discover 50 B2B accounts that closely match your top 10 customers' tech stacks and firmographics. This tool analyzes public web data including robots.txt and OpenGraph metadata to identify lookalike accounts for targeted ABM campaigns. Input your top customer domains and desired firmographic filters to receive a ranked list of potential targets with matching technologies and company attributes.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "firmographic_filters": { "additionalProperties": false, "properties": { "employee_range": { "items": { "type": "number" }, "maxItems": 2, "minItems": 2, "type": "array" }, "geo_region": { "type": "string" }, "industry": { "type": "string" }, "revenue_range": { "items": { "type": "number" }, "maxItems": 2, "minItems": 2, "type": "array" } }, "type": "object" }, "tech_stack_keywords": { "description": "Specific technologies to match in lookalike accounts", "items": { "type": "string" }, "type": "array" }, "top_customer_domains": { "description": "List of top 10 customer domains to use as seed accounts", "items": { "format": "uri", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "top_customer_domains" ], "type": "object" }, "name": "abm_lookalike_account_finder", "outputSchema": { "properties": { "lookalike_accounts": { "items": { "properties": { "company_name": { "type": "string" }, "confidence_score": { "type": "number" }, "domain": { "format": "uri", "type": "string" }, "employee_count": { "type": "number" }, "firmographic_match_score": { "type": "number" }, "industry": { "type": "string" }, "revenue_estimate": { "type": "number" }, "tech_stack_match_score": { "type": "number" } }, "required": [ "domain", "confidence_score" ], "type": "object" }, "maxItems": 50, "type": "array" }, "matched_technologies": { "items": { "type": "string" }, "type": "array" }, "sources": { "items": { "properties": { "data_type": { "enum": [ "robots.txt", "opengraph" ], "type": "string" }, "fetched_at": { "format": "date-time", "type": "string" }, "url": { "format": "uri", "type": "string" } }, "required": [ "url", "fetched_at", "data_type" ], "type": "object" }, "type": "array" }, "stats": { "properties": { "firmographic_matches": { "type": "number" }, "tech_stack_matches": { "type": "number" }, "total_analyzed": { "type": "number" } }, "type": "object" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status", "lookalike_accounts", "warnings", "sources" ], "type": "object" } }, { "description": "Mapping d'expansion comptes — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Notion B2B Enterprise — top 30 strategic accounts · expansion plays NRR 130%+ target · Snowflake/Shopify/Vercel/Stripe analyzed. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "accounts": { "items": { "properties": { "accountName": { "maxLength": 160, "minLength": 2, "type": "string" }, "contractRenewalDate": { "type": "string" }, "currentArrEur": { "minimum": 0, "type": "number" }, "currentSeats": { "minimum": 0, "type": "integer" }, "knownTriggers": { "maxLength": 400, "type": "string" }, "modulesAdopted": { "items": { "type": "string" }, "maxItems": 15, "minItems": 1, "type": "array" }, "segment": { "enum": [ "enterprise", "mid-market", "smb" ], "type": "string" }, "usageScore": { "enum": [ "heavy", "active", "light", "dormant" ], "type": "string" } }, "required": [ "accountName", "segment", "currentArrEur", "currentSeats", "modulesAdopted", "contractRenewalDate", "usageScore" ], "type": "object" }, "maxItems": 50, "minItems": 5, "type": "array" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "arrTotalEur": { "minimum": 0, "type": "number" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "nrrCurrent": { "maximum": 200, "minimum": 50, "type": "number" }, "nrrTarget": { "maximum": 200, "minimum": 50, "type": "number" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "arrTotalEur", "nrrCurrent", "nrrTarget" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" }, "ownership": { "properties": { "aeTeamSize": { "minimum": 1, "type": "integer" }, "averageQuotaPerAeEur": { "minimum": 0, "type": "number" }, "csmTeamSize": { "minimum": 1, "type": "integer" } }, "required": [ "aeTeamSize", "csmTeamSize", "averageQuotaPerAeEur" ], "type": "object" } }, "required": [ "company", "accounts", "ownership" ], "type": "object" }, "name": "account_expansion_mapper", "outputSchema": null }, { "description": "Plan d'action ESG — Gapup agent-payable C-suite expertise (SUSTAINABILITY). Returns a structured, audited deliverable. Reference case: TechCorp SAS — Plan ESG 36 mois (500 FTE, €60M CA, score 54→76/100). Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "ambitions": { "properties": { "environmental": { "maxLength": 400, "minLength": 10, "type": "string" }, "governance": { "maxLength": 400, "minLength": 10, "type": "string" }, "social": { "maxLength": 400, "minLength": 10, "type": "string" } }, "required": [ "environmental", "social", "governance" ], "type": "object" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "availableResources": { "properties": { "budgetEur": { "minimum": 0, "type": "number" }, "dedicatedFTE": { "minimum": 0, "type": "number" }, "externalPartners": { "items": { "type": "string" }, "maxItems": 6, "type": "array" } }, "required": [ "budgetEur", "dedicatedFTE" ], "type": "object" }, "company": { "properties": { "country": { "maxLength": 80, "minLength": 2, "type": "string" }, "fte": { "minimum": 1, "type": "integer" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "revenueEur": { "minimum": 0, "type": "number" }, "sector": { "maxLength": 160, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "fte", "revenueEur", "country" ], "type": "object" }, "currentScores": { "properties": { "environmental": { "maximum": 100, "minimum": 0, "type": "integer" }, "governance": { "maximum": 100, "minimum": 0, "type": "integer" }, "overallEsg": { "maximum": 100, "minimum": 0, "type": "integer" }, "social": { "maximum": 100, "minimum": 0, "type": "integer" } }, "type": "object" }, "focus": { "maxLength": 500, "type": "string" }, "horizon": { "default": "36 mois", "enum": [ "12 mois", "24 mois", "36 mois" ], "type": "string" }, "targetLabels": { "items": { "enum": [ "EcoVadis Bronze", "EcoVadis Silver", "EcoVadis Gold", "EcoVadis Platinum", "B-Corp", "Entreprise à mission", "ISO 14001", "ISO 26000", "SBTi" ], "type": "string" }, "type": "array" } }, "required": [ "company", "ambitions", "availableResources", "horizon" ], "type": "object" }, "name": "action_plan_esg", "outputSchema": null }, { "description": "An asynchronous risk assessment tool that evaluates AI model resilience against adversarial inputs following NIST AI Risk Management Framework (RMF) red-teaming protocols. Designed for security and compliance personas, it accepts model outputs or decision boundaries and returns structured risk scores, failure modes, and adversarial examples. Requires async:true to avoid timeout errors. Outputs include status, warnings, and source references.", "inputSchema": { "properties": { "adversarialDataset": { "description": "Optional custom adversarial inputs to test", "items": { "type": "string" }, "type": "array" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "maxTests": { "default": 10, "description": "Maximum number of adversarial tests to run", "maximum": 100, "minimum": 1, "type": "integer" }, "modelOutput": { "description": "The AI model's output or decision to be stress-tested", "type": "string" }, "sensitivityThreshold": { "default": 0.9, "description": "Threshold for flagging high-risk adversarial examples", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "modelOutput" ], "type": "object" }, "name": "adversarial_input_stress_tester", "outputSchema": { "properties": { "adversarialExamples": { "items": { "properties": { "confidenceDrop": { "type": "number" }, "input": { "type": "string" }, "isCritical": { "type": "boolean" }, "perturbation": { "type": "string" } }, "type": "object" }, "type": "array" }, "failureModes": { "items": { "type": "string" }, "type": "array" }, "riskScore": { "description": "Normalized risk score from adversarial testing", "maximum": 1, "minimum": 0, "type": "number" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Analyzes affiliate clickstream data from Common Crawl to flag potential fraud patterns (duplicate IPs, rapid clicks, device spoofing). Designed for CMOs to validate affiliate traffic quality and prevent budget waste. Inputs: affiliate network name and date range. Outputs: fraud probability score, suspicious IP list, and pattern analysis. Keywords: affiliate fraud detection, clickstream analysis, marketing attribution, traffic validation.", "inputSchema": { "properties": { "affiliate_network": { "description": "Name of the affiliate network to analyze (e.g., 'CJ Affiliate', 'Rakuten')", "type": "string" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "date_range": { "properties": { "end": { "description": "End date in YYYY-MM-DD format", "format": "date", "type": "string" }, "start": { "description": "Start date in YYYY-MM-DD format", "format": "date", "type": "string" } }, "required": [ "start", "end" ], "type": "object" }, "threshold": { "default": 0.7, "description": "Fraud probability threshold (0.1-0.99)", "maximum": 0.99, "minimum": 0.1, "type": "number" } }, "required": [ "affiliate_network", "date_range" ], "type": "object" }, "name": "affiliate_fraud_clickstream_detector", "outputSchema": { "properties": { "fraud_probability": { "description": "Overall fraud probability score (0-1)", "maximum": 1, "minimum": 0, "type": "number" }, "patterns_detected": { "items": { "properties": { "confidence": { "maximum": 1, "minimum": 0, "type": "number" }, "count": { "type": "number" }, "type": { "enum": [ "ip_duplication", "rapid_clicks", "device_spoofing", "time_pattern" ], "type": "string" } }, "required": [ "type", "count", "confidence" ], "type": "object" }, "type": "array" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "suspicious_ips": { "items": { "properties": { "count": { "type": "number" }, "first_seen": { "format": "date-time", "type": "string" }, "ip": { "type": "string" }, "last_seen": { "format": "date-time", "type": "string" } }, "required": [ "ip", "count" ], "type": "object" }, "type": "array" }, "total_clicks_analyzed": { "type": "number" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "As a COO, analyze non-tariff trade barriers (NTBs) across African trade corridors using WITS and UNCTAD STAT data. Input origin/destination countries and product HS codes to receive barrier mapping with severity scores and actionable mitigation strategies. Returns structured risk assessment, regulatory compliance gaps, and supply chain optimization recommendations. Pass async:true to avoid timeout.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "destination_country": { "description": "ISO 3-letter country code for import destination", "type": "string" }, "hs_code": { "description": "6-digit Harmonized System product code", "type": "string" }, "include_regulatory_details": { "default": false, "description": "Whether to include detailed regulatory text in output", "type": "boolean" }, "origin_country": { "description": "ISO 3-letter country code for export origin", "type": "string" } }, "required": [ "origin_country", "destination_country" ], "type": "object" }, "name": "africa_trade_barrier_breaker", "outputSchema": { "properties": { "barrier_summary": { "properties": { "severity_score": { "maximum": 100, "minimum": 0, "type": "number" }, "top_barrier_types": { "items": { "type": "string" }, "type": "array" }, "total_barriers": { "type": "number" } }, "type": "object" }, "mitigation_strategies": { "items": { "properties": { "estimated_impact": { "enum": [ "high", "medium", "low" ], "type": "string" }, "implementation_steps": { "items": { "type": "string" }, "type": "array" }, "strategy": { "type": "string" } }, "type": "object" }, "type": "array" }, "regulatory_details": { "items": { "properties": { "enforcement_agency": { "type": "string" }, "regulation_id": { "type": "string" }, "regulation_text": { "type": "string" } }, "type": "object" }, "type": "array" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "trade_flow_impact": { "properties": { "additional_cost_percentage": { "type": "number" }, "potential_delay_days": { "type": "number" } }, "type": "object" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status", "barrier_summary", "mitigation_strategies", "warnings", "sources" ], "type": "object" } }, { "description": "As a COO, evaluate ESG compliance of African trade finance providers using World Bank WITS trade statistics and CDP climate disclosure data. Input the financial institution's name or identifier, and receive an ESG rating with breakdown across environmental, social, and governance dimensions. Ideal for due diligence on trade partners or portfolio risk assessment. Pass async:true to avoid timeout.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "countryCode": { "description": "ISO 2-letter country code (e.g., 'ZA' for South Africa)", "pattern": "^[A-Z]{2}$", "type": "string" }, "institutionName": { "description": "Full name of the trade finance provider (e.g., 'Standard Bank Group')", "type": "string" }, "year": { "description": "Assessment year (2018-2023)", "maximum": 2023, "minimum": 2018, "type": "number" } }, "required": [ "institutionName" ], "type": "object" }, "name": "africa_trade_finance_esg_rater", "outputSchema": { "properties": { "carbonIntensity": { "description": "CO2 emissions per million USD financed (tons)", "type": "number" }, "environmentalScore": { "maximum": 100, "minimum": 0, "type": "number" }, "esgRating": { "enum": [ "A", "B", "C", "D", "E" ], "type": "string" }, "governanceScore": { "maximum": 100, "minimum": 0, "type": "number" }, "socialScore": { "maximum": 100, "minimum": 0, "type": "number" }, "sources": { "items": { "properties": { "dataset": { "type": "string" }, "fetched_at": { "format": "date-time", "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at", "dataset" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "tradeVolume": { "description": "Annual trade finance volume (USD)", "type": "number" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status", "esgRating", "warnings", "sources" ], "type": "object" } }, { "description": "Analyzes AGOA (African Growth and Opportunity Act) and EBA (Everything But Arms) trade preference arbitrage opportunities for COOs evaluating export strategies. Compares tariff rates, trade volumes, and preference utilization across eligible African countries using WITS and OECD trade data. Returns structured analysis of potential duty savings, market access advantages, and compliance requirements. — pass async:true REQUIRED to avoid x402 timeout.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "exporting_country": { "description": "ISO 2-letter country code of African exporter", "pattern": "^[A-Z]{2}$", "type": "string" }, "hs_code": { "description": "6-10 digit Harmonized System product code", "pattern": "^[0-9]{6,10}$", "type": "string" }, "importing_country": { "description": "ISO 2-letter country code of target market (US/EU)", "pattern": "^[A-Z]{2}$", "type": "string" }, "preference_scheme": { "description": "Trade preference scheme to analyze", "enum": [ "AGOA", "EBA" ], "type": "string" }, "year": { "description": "Reference year for trade data", "maximum": 2026, "minimum": 2010, "type": "integer" } }, "required": [ "hs_code", "exporting_country" ], "type": "object" }, "name": "africa_trade_preference_arbitrage", "outputSchema": { "properties": { "compliance_requirements": { "description": "List of compliance requirements for preference eligibility", "items": { "type": "string" }, "type": "array" }, "duty_savings_pct": { "description": "Estimated duty savings percentage under preference scheme", "maximum": 100, "minimum": 0, "type": "number" }, "market_access_score": { "description": "Composite score of market access advantage (0-100)", "maximum": 100, "minimum": 0, "type": "number" }, "preference_utilization_rate": { "description": "Percentage of eligible exports utilizing preference", "maximum": 100, "minimum": 0, "type": "number" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "trade_volume_usd": { "description": "Annual trade volume in USD for given HS code", "minimum": 0, "type": "number" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "As a COO, analyze AGOA/EBA duty savings opportunities with HS code-level trade route optimization. Input origin country, destination country, and HS code to receive duty savings estimates, optimal trade routes, and preference utilization recommendations. Uses UN Comtrade trade flow data, WCO tariff schedules, and African Union trade agreement rules. Ideal for export market evaluation, supply chain optimization, and trade agreement compliance analysis. Keywords: AGOA, EBA, duty savings, trade optimization, HS code, African trade, export strategy.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "destinationCountry": { "description": "ISO 3166-1 alpha-3 country code of import destination (e.g., 'USA' for United States)", "type": "string" }, "hsCode": { "description": "6-10 digit Harmonized System code (e.g., '010121' for live horses)", "type": "string" }, "originCountry": { "description": "ISO 3166-1 alpha-3 country code of export origin (e.g., 'KEN' for Kenya)", "type": "string" }, "quantity": { "description": "Estimated annual export quantity in units", "minimum": 1, "type": "number" }, "valueUsd": { "description": "Estimated annual export value in USD", "minimum": 1, "type": "number" } }, "required": [ "originCountry", "destinationCountry", "hsCode" ], "type": "object" }, "name": "africa_trade_preference_optimizer", "outputSchema": { "properties": { "alternativeRoutes": { "items": { "properties": { "dutyRate": { "type": "number" }, "path": { "items": { "type": "string" }, "type": "array" }, "preferenceProgram": { "enum": [ "AGOA", "EBA", "AfCFTA", "Bilateral", "MFN" ], "type": "string" }, "savingsVsMfn": { "description": "Duty savings compared to MFN rate in USD", "type": "number" } }, "type": "object" }, "type": "array" }, "complianceWarnings": { "description": "Potential compliance risks or documentation requirements", "items": { "type": "string" }, "type": "array" }, "dutySavings": { "description": "Estimated annual duty savings in USD under optimal preference program", "type": "number" }, "optimalRoute": { "properties": { "dutyRate": { "description": "Applied duty rate percentage", "type": "number" }, "path": { "description": "ISO 3166-1 alpha-3 country codes in optimal trade route", "items": { "type": "string" }, "type": "array" }, "preferenceProgram": { "description": "Trade preference program used", "enum": [ "AGOA", "EBA", "AfCFTA", "Bilateral", "MFN" ], "type": "string" }, "rulesOfOriginMet": { "description": "Whether product meets rules of origin requirements", "type": "boolean" } }, "type": "object" }, "sources": { "items": { "properties": { "fetched_at": { "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Intelligence préférentielle AGOA (US→Africa) et EBA/GSP (EU→Africa). Vérifie l'éligibilité d'un pays africain aux programmes tarifaires préférentiels, l'éligibilité d'un produit par code HS, identifie les meilleures opportunités d'export Afrique→US/EU, et fournit les règles de conformité (rules of origin, valeur ajoutée, docs). Différenciateur Africa diaspora : 39 pays AGOA + 47 LDCs EBA encodés. Sources : AGOA.info · EU EBA · EU GSP+ · WTO Tariff · UN Comtrade.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "country_iso": { "description": "Code ISO 2-lettres du pays africain (requis pour country_eligibility). Exemples : KE=Kenya, NG=Nigeria, ZA=Afrique du Sud, ET=Éthiopie, LS=Lesotho, GH=Ghana.", "type": "string" }, "destination": { "description": "Marché de destination pour trade_opportunity : 'US', 'EU', ou 'both' (défaut). Ignoré pour les autres modes.", "enum": [ "US", "EU", "both" ], "type": "string" }, "hs_code": { "description": "Code HS (Harmonized System) 6+ chiffres (requis pour product_eligibility). Exemple : '620342' = pantalons coton homme, '090111' = café arabica non torréfié, '060310' = fleurs fraîches.", "type": "string" }, "mode": { "description": "Mode d'analyse : 'country_eligibility' (statut AGOA/EBA/GSP d'un pays africain) | 'product_eligibility' (éligibilité d'un produit par code HS) | 'trade_opportunity' (top opportunités export Afrique→US/EU) | 'compliance_check' (rules of origin, seuils valeur ajoutée, documentation)", "enum": [ "country_eligibility", "product_eligibility", "trade_opportunity", "compliance_check" ], "type": "string" } }, "required": [ "mode" ], "type": "object" }, "name": "agoa_eba_intelligence", "outputSchema": null }, { "description": "Generates EU AI Act incident response playbooks with regulator notification templates for risk management teams. Inputs include incident severity, AI system type, and affected stakeholders. Outputs structured playbook steps, regulator notification drafts, and compliance checklists. Essential for high-risk AI system breaches requiring formal EU notification — pass async:true REQUIRED to avoid x402 timeout. Keywords: AI Act compliance, incident response, regulator notification, risk management, ISO 27035, NIST SP 800-61.", "inputSchema": { "properties": { "affected_stakeholders": { "items": { "type": "string" }, "type": "array" }, "ai_system_type": { "enum": [ "high_risk", "limited_risk", "minimal_risk" ], "type": "string" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "incident_description": { "type": "string" }, "incident_type": { "enum": [ "bias", "safety_failure", "data_breach", "model_failure", "other" ], "type": "string" }, "severity": { "enum": [ "low", "medium", "high", "critical" ], "type": "string" } }, "required": [ "incident_type", "severity" ], "type": "object" }, "name": "ai_act_incident_response", "outputSchema": { "properties": { "compliance_checklist": { "items": { "properties": { "evidence": { "type": "string" }, "met": { "type": "boolean" }, "requirement": { "type": "string" } }, "type": "object" }, "type": "array" }, "next_steps": { "items": { "type": "string" }, "type": "array" }, "playbook_steps": { "items": { "properties": { "description": { "type": "string" }, "responsible_party": { "type": "string" }, "step": { "type": "string" } }, "type": "object" }, "type": "array" }, "regulator_notification": { "properties": { "attachments": { "items": { "type": "string" }, "type": "array" }, "body": { "type": "string" }, "subject": { "type": "string" } }, "type": "object" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "A legal-focused tool for simulating EU AI Act regulatory sandbox submissions. Provides structured feedback on compliance, risk levels, and required documentation based on EUR-Lex and OECD AI Policy Observatory sources. Accepts AI system descriptions, intended use cases, and technical specifications as input. Returns detailed assessment with warnings, citations, and actionable recommendations for legal teams and AI developers.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "documentation": { "description": "List of provided documentation types (e.g., 'technical', 'ethical', 'data')", "items": { "type": "string" }, "type": "array" }, "intendedUse": { "description": "Primary and secondary use cases of the AI system", "type": "string" }, "riskLevel": { "description": "Self-assessed risk level of the AI system", "enum": [ "minimal", "limited", "high", "unacceptable" ], "type": "string" }, "sector": { "description": "Primary sector of application", "enum": [ "healthcare", "finance", "transport", "education", "public", "other" ], "type": "string" }, "systemDescription": { "description": "Detailed description of the AI system including purpose, architecture, and data sources", "type": "string" } }, "required": [ "systemDescription", "intendedUse", "riskLevel" ], "type": "object" }, "name": "ai_act_sandbox_regulatory_sandbox", "outputSchema": { "properties": { "assessment": { "properties": { "citations": { "items": { "type": "string" }, "type": "array" }, "complianceScore": { "maximum": 100, "minimum": 0, "type": "number" }, "documentationGaps": { "items": { "type": "string" }, "type": "array" }, "requiredActions": { "items": { "type": "string" }, "type": "array" }, "riskLevel": { "enum": [ "minimal", "limited", "high", "unacceptable" ], "type": "string" } }, "required": [ "complianceScore", "riskLevel", "requiredActions" ], "type": "object" }, "sources": { "items": { "properties": { "fetched_at": { "type": "string" }, "title": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "As a CTO, audit AI training datasets for EU AI Act compliance with bias detection and regulatory risk assessment. Inputs: dataset identifier (Hugging Face ID or URL) and optional risk thresholds. Outputs: compliance score, bias metrics, regulatory warnings, and source references. Ideal for pre-deployment risk evaluation. Pass async:true to avoid timeout.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "dataset_id": { "description": "Hugging Face dataset identifier or direct URL to dataset", "type": "string" }, "include_bias_metrics": { "default": true, "type": "boolean" }, "risk_threshold": { "default": 0.7, "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "dataset_id" ], "type": "object" }, "name": "ai_act_training_data_audit", "outputSchema": { "properties": { "bias_metrics": { "properties": { "age_bias": { "maximum": 1, "minimum": 0, "type": "number" }, "gender_bias": { "maximum": 1, "minimum": 0, "type": "number" }, "racial_bias": { "maximum": 1, "minimum": 0, "type": "number" } }, "type": "object" }, "compliance_score": { "maximum": 1, "minimum": 0, "type": "number" }, "dataset_metadata": { "properties": { "license": { "type": "string" }, "name": { "type": "string" }, "size": { "type": "number" } }, "type": "object" }, "regulatory_warnings": { "items": { "type": "string" }, "type": "array" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "source_type": { "enum": [ "huggingface", "eu_ai_act", "google_dataset" ], "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Audit EU AI Act complet (Règlement UE 2024/1689) — implémentation native audit-grade. Classifie le système IA selon les 4 tiers de risque (unacceptable/high_risk/limited_risk/minimal_risk/gpai) sur la base de l'Annexe III et de l'Article 5. Produit : (1) classification tier + justification + articles applicables, (2) checklist conformité Articles 9-15 + 50 + 53-55, (3) gaps documentation Annexe IV, (4) mapping ISO 42001, (5) deadlines EU AI Act 2025-2029, (6) estimation coût et effort, (7) top 10 recommandations P0/P1/P2. Retourne immédiatement (<300ms) un job_id. Poller avec ai_governance_full_report_result(job_id) après eta_seconds (~90s). Cache 7 jours pour inputs identiques. Async tool — register a webhook via `webhooks_manage(register, url, [job.completed])` to receive callbacks instead of polling. Faster + lighter. DISCLAIMER : non substitutif à un avis juridique professionnel.", "inputSchema": { "properties": { "affected_persons": { "description": "Catégories de personnes affectées par les décisions du système (ex: candidats, employés, clients)", "items": { "type": "string" }, "type": "array" }, "ai_system_description": { "description": "Description détaillée du système IA : ce qu'il fait, comment il fonctionne, quelles décisions il prend", "maxLength": 2000, "minLength": 10, "type": "string" }, "company_size": { "description": "Taille entreprise : startup (≤50), smb (51-250), mid (251-1000), large (1001-5000), enterprise (>5000)", "enum": [ "startup", "smb", "mid", "large", "enterprise" ], "type": "string" }, "data_sources": { "description": "Sources de données utilisées par le système IA", "items": { "type": "string" }, "type": "array" }, "deployment_context": { "description": "Contexte de déploiement : interne (usage employés), public, B2B, B2C", "enum": [ "internal", "public", "b2b", "b2c" ], "type": "string" }, "geographic_scope": { "description": "Zones géographiques de déploiement (ex: 'EU', 'France', 'Global')", "items": { "type": "string" }, "type": "array" }, "intended_purpose": { "description": "Finalité prévue du système IA : à quoi sert-il concrètement", "maxLength": 500, "minLength": 5, "type": "string" } }, "required": [ "ai_system_description", "intended_purpose" ], "type": "object" }, "name": "ai_governance_full_report_async", "outputSchema": { "properties": { "eta_seconds": { "description": "Durée estimée avant disponibilité du résultat", "type": "number" }, "job_id": { "description": "Identifiant unique du job — passer à ai_governance_full_report_result", "type": "string" }, "status": { "enum": [ "queued" ], "type": "string" }, "submitted_at": { "description": "Timestamp ISO-8601 de soumission", "type": "string" } }, "required": [ "job_id", "status", "eta_seconds", "submitted_at" ], "type": "object" } }, { "description": "Poll the result of an ai_governance_full_report_async job. Returns status=pending while running, status=completed with the full EU AI Act governance audit report once done (risk_tier, compliance checklist Articles 9-15/50/53-55, Annex IV documentation gaps, ISO 42001 alignment, deadlines 2025-2029, cost estimate, top-10 recommendations P0/P1/P2, compliance_score), status=failed on error, or status=not_found if the job_id is unknown or expired (TTL 24h). Call this after the eta_seconds hint returned by ai_governance_full_report_async (~90s).", "inputSchema": { "properties": { "job_id": { "description": "The job_id returned by ai_governance_full_report_async (prefix: aigfr_)", "type": "string" } }, "required": [ "job_id" ], "type": "object" }, "name": "ai_governance_full_report_result", "outputSchema": { "oneOf": [ { "properties": { "elapsed_seconds": { "type": "number" }, "eta_seconds": { "type": [ "number", "null" ] }, "status": { "enum": [ "pending" ], "type": "string" } }, "required": [ "status", "elapsed_seconds" ], "title": "Pending" }, { "properties": { "result": { "description": "Rapport d'audit EU AI Act complet", "properties": { "_disclaimer": { "type": "string" }, "_generated_at": { "type": "string" }, "_version": { "type": "string" }, "checklist": { "description": "Checklist conformité Articles 9-15, 50, 53-55 avec statut et preuves requises", "items": { "properties": { "article": { "type": "string" }, "current_evidence": { "type": "string" }, "evidence_needed": { "items": { "type": "string" }, "type": "array" }, "requirement": { "type": "string" }, "status": { "enum": [ "compliant", "gap", "partial", "not_applicable" ], "type": "string" } }, "type": "object" }, "type": "array" }, "compliance_score": { "properties": { "critical_gaps": { "type": "number" }, "major_gaps": { "type": "number" }, "minor_gaps": { "type": "number" }, "overall_pct": { "type": "number" } }, "type": "object" }, "cost_estimate": { "description": "Estimation coût et effort par tier × taille entreprise", "properties": { "annual_maintenance_eur": { "type": "number" }, "description": { "type": "string" }, "initial_cost_eur_max": { "type": "number" }, "initial_cost_eur_min": { "type": "number" }, "months_max": { "type": "number" }, "months_min": { "type": "number" } }, "type": "object" }, "deadlines": { "description": "Échéances EU AI Act 2025-2029 avec jours restants et urgence", "items": { "properties": { "applicable_to": { "items": { "type": "string" }, "type": "array" }, "article": { "type": "string" }, "date": { "type": "string" }, "days_remaining": { "type": "number" }, "iso_date": { "type": "string" }, "requirement": { "type": "string" }, "urgency": { "enum": [ "overdue", "critical", "high", "medium", "low" ], "type": "string" } }, "type": "object" }, "type": "array" }, "documentation_gaps": { "description": "Lacunes documentation Annexe IV par section et sévérité (critical/major/minor)", "items": { "properties": { "section": { "type": "string" }, "severity": { "enum": [ "critical", "major", "minor" ], "type": "string" }, "title": { "type": "string" }, "what_to_produce": { "type": "string" } }, "type": "object" }, "type": "array" }, "input_summary": { "type": "object" }, "iso_42001_alignment": { "description": "Mapping contrôles ISO 42001 couverts vs gaps", "properties": { "coverage_pct": { "type": "number" }, "covered_controls": { "items": { "type": "string" }, "type": "array" }, "gap_controls": { "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "recommendations": { "description": "Top 10 actions P0/P1/P2 par ordre de priorité", "items": { "properties": { "action": { "type": "string" }, "deadline": { "type": "string" }, "estimated_effort": { "type": "string" }, "priority": { "enum": [ "P0", "P1", "P2" ], "type": "string" }, "rationale": { "type": "string" } }, "type": "object" }, "type": "array" }, "risk_classification": { "description": "Tier EU AI Act : unacceptable | high_risk | limited_risk | minimal_risk | gpai", "properties": { "applicable_articles": { "items": { "type": "string" }, "type": "array" }, "confidence": { "enum": [ "high", "medium", "low" ], "type": "string" }, "matched_annex_iii_category": { "type": "string" }, "prohibited_practice_flags": { "items": { "type": "string" }, "type": "array" }, "risk_tier": { "enum": [ "unacceptable", "high_risk", "limited_risk", "minimal_risk", "gpai" ], "type": "string" }, "tier_justification": { "type": "string" }, "tier_label": { "type": "string" } }, "type": "object" } }, "type": "object" }, "status": { "enum": [ "completed" ], "type": "string" }, "took_seconds": { "type": "number" } }, "required": [ "status", "result", "took_seconds" ], "title": "Completed" }, { "properties": { "error": { "type": "string" }, "status": { "enum": [ "failed" ], "type": "string" }, "took_seconds": { "type": "number" } }, "required": [ "status", "error", "took_seconds" ], "title": "Failed" }, { "properties": { "status": { "enum": [ "not_found" ], "type": "string" } }, "required": [ "status" ], "title": "NotFound" } ], "type": "object" } }, { "description": "Pilotage de gouvernance IA — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Reference case: TalentScope SAS — scoring IA candidats RH (EU AI Act Annex III §4, high-risk). Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "aiUseCases": { "items": { "properties": { "dataTypes": { "items": { "type": "string" }, "maxItems": 10, "type": "array" }, "description": { "maxLength": 500, "type": "string" }, "impactedUsers": { "maxLength": 200, "type": "string" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" } }, "required": [ "name", "description", "dataTypes", "impactedUsers" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "country": { "maxLength": 80, "minLength": 2, "type": "string" }, "fte": { "minimum": 1, "type": "integer" }, "jurisdictions": { "items": { "enum": [ "FR", "EU", "UK", "US-CA", "other" ], "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "revenueEur": { "minimum": 0, "type": "number" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "fte", "revenueEur", "country", "jurisdictions" ], "type": "object" }, "focus": { "maxLength": 500, "type": "string" }, "targetFrameworks": { "items": { "enum": [ "EU AI Act", "ISO 42001", "NIST AI RMF", "GDPR Art.22", "HIPAA", "AGIRC-ARRCO", "UK AI Code" ], "type": "string" }, "maxItems": 7, "minItems": 1, "type": "array" } }, "required": [ "company", "aiUseCases", "targetFrameworks" ], "type": "object" }, "name": "ai_governance_pilot", "outputSchema": null }, { "description": "Bouclier anti-démissions — Gapup agent-payable C-suite expertise (COO). Returns a structured, audited deliverable. Reference case: Buffer Inc — détection des at-risk parmi 80 FTEs (Q1 2026). Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "fte": { "minimum": 1, "type": "integer" }, "fundingStage": { "type": "string" }, "location": { "type": "string" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "fte" ], "type": "object" }, "employees": { "items": { "properties": { "compensationPercentile": { "maximum": 100, "minimum": 0, "type": "number" }, "id": { "type": "string" }, "lastEngagementScore": { "maximum": 100, "minimum": 0, "type": "number" }, "lastPerfRating": { "type": "string" }, "level": { "enum": [ "IC", "manager", "director", "VP", "C-level" ], "type": "string" }, "recentSignals": { "items": { "type": "string" }, "maxItems": 10, "type": "array" }, "role": { "type": "string" }, "team": { "type": "string" }, "tenure": { "minimum": 0, "type": "integer" } }, "required": [ "id", "role", "level", "tenure", "team" ], "type": "object" }, "maxItems": 200, "minItems": 1, "type": "array" }, "focus": { "maxLength": 400, "type": "string" }, "signals": { "properties": { "eNPS": { "maximum": 100, "minimum": -100, "type": "number" }, "engagementScore": { "maximum": 100, "minimum": 0, "type": "number" }, "externalNoise": { "items": { "type": "string" }, "maxItems": 10, "type": "array" }, "recentLayoffs": { "type": "boolean" }, "recentLeadershipChange": { "type": "boolean" } }, "type": "object" } }, "required": [ "company", "signals", "employees" ], "type": "object" }, "name": "anti_demissions_hr", "outputSchema": null }, { "description": "Commercial arbitration intelligence for litigation lawyers, M&A due diligence teams, sovereign wealth funds and trade finance compliance. Covers 8 major institutions: ICC, AAA, LCIA, HKIAC, SIAC, CIETAC, DIAC, ICDR.\n\nThree modes:\n• party_lookup — find awards by party name (searches 20 landmark public awards + JusMundi best-effort)\n• institution_index — browse awards and caseload stats per institution with date range filter\n• clause_check — audit an arbitration clause for missing elements (institution, seat, language, arbitrator count, governing law, binding nature)\n\nNote: Most arbitration awards are confidential. This tool surfaces public awards (Yukos, Crystallex, Achmea, etc.) plus redacted statistics from institutional annual reports. Private awards are not accessible.\n\nCache: 24h (arbitration data is very stable). No API key required.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "date_from": { "description": "ISO date filter from (YYYY-MM-DD). Applied to award_date.", "type": "string" }, "date_to": { "description": "ISO date filter to (YYYY-MM-DD). Applied to award_date.", "type": "string" }, "institution": { "description": "Filter by institution. Default 'all'.", "enum": [ "ICC", "AAA", "LCIA", "HKIAC", "SIAC", "CIETAC", "DIAC", "ICDR", "all" ], "type": "string" }, "mode": { "description": "party_lookup: search by party name or keyword. institution_index: browse awards by institution + stats. clause_check: audit an arbitration clause for issues.", "enum": [ "party_lookup", "institution_index", "clause_check" ], "type": "string" }, "query": { "description": "For party_lookup: party name or keyword (e.g. \"Yukos\", \"Russia\"). For institution_index: institution name or keyword. For clause_check: full text of the arbitration clause to audit.", "maxLength": 2000, "minLength": 3, "type": "string" } }, "required": [ "mode", "query" ], "type": "object" }, "name": "arbitration_awards_lookup", "outputSchema": { "properties": { "awards": { "items": { "properties": { "award_amount_usd": { "type": "number" }, "award_date": { "type": "string" }, "case_id": { "type": "string" }, "enforcement_status": { "type": "string" }, "governing_law": { "type": "string" }, "industry": { "type": "string" }, "institution": { "type": "string" }, "parties": { "items": { "type": "string" }, "type": "array" }, "publication_status": { "type": "string" }, "seat": { "type": "string" }, "source_url": { "type": "string" }, "summary": { "type": "string" } }, "required": [ "case_id", "institution", "parties", "seat", "governing_law", "publication_status" ], "type": "object" }, "type": "array" }, "clause_check": { "properties": { "drafting_suggestions": { "items": { "type": "string" }, "type": "array" }, "institution_identified": { "type": "string" }, "issues_detected": { "items": { "type": "string" }, "type": "array" }, "rules_version": { "type": "string" }, "seat_identified": { "type": "string" } }, "required": [ "issues_detected", "drafting_suggestions" ], "type": "object" }, "institution_stats": { "properties": { "average_award_duration_months": { "type": "number" }, "common_seats": { "items": { "type": "string" }, "type": "array" }, "institution": { "type": "string" }, "total_caseload_year": { "type": "number" } }, "required": [ "institution", "common_seats" ], "type": "object" }, "mode": { "type": "string" }, "quality_score": { "type": "number" }, "query": { "type": "string" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "partial", "unavailable" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" } }, "required": [ "mode", "query", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Surveillance surface d'attaque — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Answers: Which Internet-facing assets of <domain> combine a critical CVE, an exposed service, and no WAF — top findings to fix in 14 days? · What is the attack surface of <domain>: subdomains, open ports, SSL/TLS grades, and associated CVEs? · Give me a CISO-ready ASM report with blast radius estimate and SLA-driven remediation plan for <domain>. · What is the email phishing risk for <domain>? Assess SPF/DMARC posture and recommend improvements. · During M&A due diligence, what are the top cyber exposures on <domain>'s Internet-facing infrastructure? Reference case: Velora Payments — 8 assets exposés · 2 critiques (CVE-2023-44487 HTTP/2 RapidReset, Admin panel ouvert) · . Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "domain": { "maxLength": 253, "minLength": 4, "type": "string" }, "exclusions": { "items": { "maxLength": 253, "type": "string" }, "maxItems": 20, "type": "array" }, "focus": { "maxLength": 500, "type": "string" }, "include_email_surface": { "default": true, "type": "boolean" }, "scope_cidrs": { "items": { "type": "string" }, "maxItems": 5, "type": "array" } }, "required": [ "domain", "include_email_surface" ], "type": "object" }, "name": "attack_surface_monitor", "outputSchema": null }, { "description": "Pré-audit comptable — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Reference case: Spendesk — Pré-audit commissaire · Readiness 74/100 · 4 findings critiques · Checklist 18 docs. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "audit": { "properties": { "auditor": { "maxLength": 100, "type": "string" }, "scheduledDate": { "type": "string" }, "scope": { "maxLength": 300, "minLength": 5, "type": "string" }, "type": { "enum": [ "statutory", "internal", "due-diligence", "ipo-readiness", "sox" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" }, "stage": { "enum": [ "pre-seed", "seed", "serie-a", "serie-b", "serie-c", "serie-d-plus", "profitable-sme", "enterprise" ], "type": "string" } }, "required": [ "name", "sector", "stage" ], "type": "object" }, "financials": { "properties": { "annualRevenueEur": { "minimum": 0, "type": "number" }, "closingDateLastYear": { "type": "string" }, "totalAssetsEur": { "minimum": 0, "type": "number" } }, "required": [ "annualRevenueEur", "totalAssetsEur" ], "type": "object" }, "knownIssues": { "items": { "maxLength": 300, "minLength": 5, "type": "string" }, "maxItems": 6, "minItems": 0, "type": "array" }, "systems": { "items": { "maxLength": 100, "type": "string" }, "maxItems": 8, "minItems": 0, "type": "array" } }, "required": [ "company", "audit", "financials", "knownIssues", "systems" ], "type": "object" }, "name": "audit_pre_flight", "outputSchema": null }, { "description": "As a CFO-focused tool, banking_fee_negotiator analyzes your bank's fee structures (account maintenance, wire transfers, credit lines) and provides data-driven negotiation recommendations. Input your current fees and bank details to receive benchmark comparisons from World Bank and ECB SDW, along with specific levers to reduce costs. Ideal for optimizing treasury operations and improving financial efficiency. Keywords: bank fees, cost optimization, treasury management, financial benchmarking, negotiation strategy.", "inputSchema": { "properties": { "account_maintenance_fee": { "description": "Current monthly account maintenance fee in USD", "type": "number" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "bank_country": { "description": "ISO 2-letter country code of the bank", "type": "string" }, "credit_line_fee": { "description": "Current annual credit line fee percentage", "type": "number" }, "industry": { "description": "Industry classification (e.g., 'manufacturing', 'retail')", "type": "string" }, "international_wire_fee": { "description": "Current international wire transfer fee in USD", "type": "number" }, "wire_transfer_fee": { "description": "Current domestic wire transfer fee in USD", "type": "number" } }, "required": [ "account_maintenance_fee", "bank_country" ], "type": "object" }, "name": "banking_fee_negotiator", "outputSchema": { "properties": { "account_maintenance_benchmark": { "description": "Regional benchmark for account maintenance fees in USD", "type": "number" }, "credit_line_benchmark": { "description": "Industry benchmark for credit line fees percentage", "type": "number" }, "international_wire_benchmark": { "description": "Regional benchmark for international wire transfer fees in USD", "type": "number" }, "negotiation_levers": { "items": { "properties": { "description": { "type": "string" }, "lever": { "type": "string" }, "potential_savings": { "type": "number" } }, "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "dataset": { "type": "string" }, "fetched_at": { "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" }, "wire_transfer_benchmark": { "description": "Regional benchmark for domestic wire transfer fees in USD", "type": "number" } }, "required": [ "status" ], "type": "object" } }, { "description": "Fiche de combat live — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Gapup Hub vs McKinsey Lilli — Deal SaaS B2B €500k · Win rate +11 pts · 6 objections clés armées. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "competitor": { "properties": { "category": { "maxLength": 120, "minLength": 2, "type": "string" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" } }, "required": [ "name", "category" ], "type": "object" }, "dealContext": { "properties": { "buyerPersona": { "maxLength": 200, "minLength": 2, "type": "string" }, "dealSizeEur": { "minimum": 0, "type": "number" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "sector", "buyerPersona" ], "type": "object" }, "knownWeaknesses": { "maxLength": 400, "type": "string" }, "ourOffer": { "maxLength": 400, "minLength": 10, "type": "string" } }, "required": [ "competitor", "dealContext", "ourOffer" ], "type": "object" }, "name": "battle_cards_live", "outputSchema": null }, { "description": "Plan de bataille marketing — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Gapup Hub — Q3 2026 · Budget €120k · Pipeline €800k · 5 chantiers prioritaires. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "arrCurrent": { "type": "number" }, "arrTarget": { "type": "number" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "budgetEur": { "type": "number" }, "companyName": { "type": "string" }, "currentBlockers": { "items": { "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" }, "icpDescription": { "type": "string" }, "primaryObjective": { "enum": [ "pipeline", "brand-awareness", "product-launch", "expansion", "retention" ], "type": "string" }, "quarter": { "type": "string" }, "teamSize": { "type": "number" }, "topChannels": { "items": { "enum": [ "content", "seo", "paid-search", "paid-social", "email", "events", "pr", "partnerships", "product-led", "outbound", "community", "influencers" ], "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" } }, "required": [ "companyName", "arrCurrent", "arrTarget", "quarter", "teamSize", "budgetEur", "primaryObjective", "topChannels", "icpDescription", "currentBlockers" ], "type": "object" }, "name": "battle_plan", "outputSchema": null }, { "description": "Tracks bias amplification in LLM outputs by analyzing fairness metrics from HuggingFace's model leaderboard. Designed for risk assessment personas to detect and quantify demographic, gender, or racial bias amplification in generated text. Accepts model identifiers or output samples, returns structured bias metrics and amplification trends.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "demographicGroups": { "description": "Specific demographic groups to monitor (e.g., ['gender', 'race'])", "items": { "type": "string" }, "type": "array" }, "modelId": { "description": "HuggingFace model identifier (e.g., 'facebook/opt-1.3b')", "type": "string" }, "outputSamples": { "description": "Array of LLM output strings to analyze for bias amplification", "items": { "type": "string" }, "type": "array" } }, "required": [], "type": "object" }, "name": "bias_amplification_tracker", "outputSchema": { "properties": { "biasMetrics": { "properties": { "amplificationScore": { "type": "number" }, "demographicParity": { "type": "number" }, "equalOpportunity": { "type": "number" }, "groupDisparities": { "additionalProperties": { "type": "number" }, "type": "object" } }, "type": "object" }, "sources": { "items": { "properties": { "fetched_at": { "type": "string" }, "model_id": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "As a CFO, quickly assess whether your bond covenants meet ESG compliance standards set by BIS and ECB. This tool analyzes covenant text against regulatory benchmarks, identifying potential ESG-related risks in carbon emissions, governance practices, and social impact clauses. Input bond covenant details and receive structured compliance insights with source references. Ideal for pre-issuance due diligence or ongoing monitoring of existing bond portfolios.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "couponType": { "description": "Type of bond coupon", "enum": [ "fixed", "floating", "zero-coupon" ], "type": "string" }, "covenantText": { "description": "Full text of the bond covenant to analyze", "type": "string" }, "issuerSector": { "description": "Industry sector of the bond issuer (e.g., energy, finance)", "type": "string" }, "jurisdiction": { "description": "Legal jurisdiction governing the bond (e.g., EU, US)", "type": "string" }, "maturityDate": { "description": "Maturity date of the bond", "format": "date", "type": "string" } }, "required": [ "covenantText" ], "type": "object" }, "name": "bond_covenant_esg_compliance_checker", "outputSchema": { "properties": { "complianceScore": { "maximum": 100, "minimum": 0, "type": "number" }, "recommendations": { "items": { "type": "string" }, "type": "array" }, "riskAreas": { "items": { "properties": { "category": { "enum": [ "Environmental", "Social", "Governance" ], "type": "string" }, "clauseReference": { "type": "string" }, "description": { "type": "string" }, "riskLevel": { "enum": [ "Low", "Medium", "High" ], "type": "string" } }, "required": [ "category", "riskLevel", "description" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "benchmark": { "enum": [ "BIS", "ECB" ], "type": "string" }, "fetched_at": { "format": "date-time", "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at", "benchmark" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "As a CFO, monitor bond covenant compliance by analyzing leverage ratios (debt-to-equity, debt-to-EBITDA) and interest coverage ratios using real-time financial data. Input a company's ticker symbol and optional covenant thresholds to receive compliance status, key financial metrics, and SEC filing references. Ideal for proactive debt management and regulatory compliance tracking. Keywords: bond covenants, leverage ratio, interest coverage, debt compliance, SEC filings, financial health.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "covenantThresholds": { "additionalProperties": false, "properties": { "maxDebtToEquity": { "description": "Maximum allowed debt-to-equity ratio", "type": "number" }, "minInterestCoverage": { "description": "Minimum required interest coverage ratio", "type": "number" } }, "type": "object" }, "ticker": { "description": "Company ticker symbol (e.g., 'AAPL')", "type": "string" } }, "required": [ "ticker" ], "type": "object" }, "name": "bond_covenant_monitor", "outputSchema": { "properties": { "complianceStatus": { "enum": [ "compliant", "non-compliant", "warning" ], "type": "string" }, "debtToEquity": { "type": "number" }, "interestCoverage": { "type": "number" }, "lastFilingDate": { "format": "date-time", "type": "string" }, "leverageRatio": { "type": "number" }, "nextFilingDeadline": { "format": "date-time", "type": "string" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "source": { "enum": [ "sec", "yahoo_finance" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at", "source" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status", "complianceStatus", "warnings", "sources" ], "type": "object" } }, { "description": "Business Plan narratif — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Reference case: Stripe Series A 2012. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "countryHQ": { "maxLength": 60, "minLength": 2, "type": "string" }, "foundedYear": { "maximum": 2030, "minimum": 1990, "type": "integer" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "pitch": { "maxLength": 800, "minLength": 20, "type": "string" }, "stage": { "enum": [ "pre-seed", "seed", "series-a", "series-b", "series-c", "growth" ], "type": "string" } }, "required": [ "name", "pitch", "foundedYear", "stage", "countryHQ" ], "type": "object" }, "keyMetrics": { "items": { "maxLength": 80, "minLength": 2, "type": "string" }, "maxItems": 6, "minItems": 2, "type": "array" }, "raise": { "properties": { "investorAudience": { "enum": [ "bank", "vc", "public-grant", "mixed" ], "type": "string" }, "targetAmountEur": { "minimum": 50000, "type": "integer" }, "timelineMonths": { "maximum": 24, "minimum": 1, "type": "integer" } }, "required": [ "targetAmountEur", "investorAudience", "timelineMonths" ], "type": "object" } }, "required": [ "company", "raise", "keyMetrics" ], "type": "object" }, "name": "bp_narratif", "outputSchema": null }, { "description": "Architecte de marque — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Pennylane — brand identity SaaS fintech B2B FR/EU (2023). Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "brand": { "properties": { "geographies": { "items": { "maxLength": 60, "minLength": 2, "type": "string" }, "maxItems": 6, "minItems": 1, "type": "array" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "sector": { "maxLength": 150, "minLength": 3, "type": "string" }, "stage": { "enum": [ "pre-seed", "seed", "series-a", "series-b", "scale-up", "established" ], "type": "string" } }, "required": [ "name", "sector", "stage", "geographies" ], "type": "object" }, "existingAssets": { "properties": { "colorHexIfAny": { "maxLength": 7, "type": "string" }, "taglineIfAny": { "maxLength": 120, "type": "string" }, "voiceDescIfAny": { "maxLength": 200, "type": "string" } }, "type": "object" }, "founder": { "properties": { "storyOrigin": { "maxLength": 600, "minLength": 20, "type": "string" }, "values": { "items": { "maxLength": 60, "minLength": 2, "type": "string" }, "maxItems": 7, "minItems": 2, "type": "array" }, "vision": { "maxLength": 300, "minLength": 10, "type": "string" } }, "required": [ "storyOrigin", "vision", "values" ], "type": "object" }, "target": { "properties": { "competitors": { "items": { "maxLength": 80, "minLength": 2, "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" }, "icp": { "maxLength": 300, "minLength": 10, "type": "string" }, "painPoints": { "items": { "maxLength": 200, "minLength": 5, "type": "string" }, "maxItems": 5, "minItems": 2, "type": "array" } }, "required": [ "icp", "painPoints", "competitors" ], "type": "object" } }, "required": [ "founder", "brand", "target" ], "type": "object" }, "name": "brand_builder", "outputSchema": null }, { "description": "Calculates brand equity voice share for CMOs by analyzing mentions across 500K+ news articles and forums from Common Crawl and Wayback Machine. Inputs include brand name, competitors, and time range. Outputs voice share percentage, sentiment distribution, and top sources. Ideal for competitive benchmarking and brand visibility tracking. Pass async:true to avoid timeout.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "brand": { "type": "string" }, "competitors": { "items": { "type": "string" }, "type": "array" }, "include_forums": { "default": true, "type": "boolean" }, "time_range": { "properties": { "end": { "format": "date-time", "type": "string" }, "start": { "format": "date-time", "type": "string" } }, "required": [ "start", "end" ], "type": "object" } }, "required": [ "brand", "time_range" ], "type": "object" }, "name": "brand_equity_voice_share_calculator", "outputSchema": { "properties": { "brand_voice_share": { "maximum": 100, "minimum": 0, "type": "number" }, "competitor_voice_shares": { "items": { "properties": { "name": { "type": "string" }, "share": { "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "name", "share" ], "type": "object" }, "type": "array" }, "sentiment_distribution": { "properties": { "negative": { "maximum": 100, "minimum": 0, "type": "number" }, "neutral": { "maximum": 100, "minimum": 0, "type": "number" }, "positive": { "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "positive", "neutral", "negative" ], "type": "object" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "top_sources": { "items": { "properties": { "mentions": { "type": "number" }, "sentiment_score": { "type": "number" }, "url": { "type": "string" } }, "required": [ "url", "mentions" ], "type": "object" }, "type": "array" }, "total_mentions": { "type": "number" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Analyse d'écart budgétaire — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Answers: Explain the key drivers of the budget vs actual variance for <company> in <period> — what are the top 10 narrative explanations? · Which cost categories drove the budget overrun for <company> in <quarter>, and what corrective actions should management take? · Revise the Q4 forecast based on observed Q3 variances for <company> — give me 3 scenarios (base, optimistic, conservative). · Prepare a board-ready budget variance memo for <company> — <period>, budget €<X>M vs actual €<Y>M, with management actions. · What are the quick wins to reduce budget overspend for <company> by end of quarter without impacting growth targets? Reference case: Doctolib Q3 2026 — budget €38.5M vs actual €41.2M (+7.0%) — cloud + headcount + deals timing. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "budgetContext": { "properties": { "actualAmount": { "minimum": 0, "type": "number" }, "categories": { "items": { "type": "string" }, "maxItems": 20, "minItems": 1, "type": "array" }, "globalVariancePct": { "type": "number" }, "notes": { "maxLength": 2000, "type": "string" }, "totalBudgetAmount": { "minimum": 0, "type": "number" } }, "required": [ "totalBudgetAmount", "actualAmount", "globalVariancePct", "categories" ], "type": "object" }, "entity": { "properties": { "currency": { "default": "EUR", "type": "string" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "period": { "maxLength": 30, "minLength": 4, "type": "string" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "currency", "period" ], "type": "object" }, "focus": { "maxLength": 500, "type": "string" } }, "required": [ "entity", "budgetContext" ], "type": "object" }, "name": "budget_variance_ai", "outputSchema": null }, { "description": "Enrich a location with European building intelligence: roof surfaces (m²), parking areas, solar-obligation status under French loi APER and loi Climat-Résilience, existing solar installations. Covers 628,000 scanned roofs and 91,800 parkings across 6 EU countries (FR, DE, IT, ES, BE, NL). Deterministic database lookup — no LLM, no generation, sub-second.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "lat": { "description": "Latitude (WGS84)", "maximum": 90, "minimum": -90, "type": "number" }, "lng": { "description": "Longitude (WGS84)", "maximum": 180, "minimum": -180, "type": "number" }, "minAreaM2": { "description": "Only return roofs/parkings at least this large, in m²", "minimum": 0, "type": "integer" }, "radiusM": { "description": "Search radius in metres (default 150, max 500)", "maximum": 500, "minimum": 10, "type": "integer" } }, "required": [ "lat", "lng" ], "type": "object" }, "name": "building_enrich", "outputSchema": { "properties": { "parkings": { "type": "array" }, "query": { "type": "object" }, "roofs": { "type": "array" }, "solarInstalled": { "type": "array" }, "summary": { "type": "object" } }, "type": "object" } }, { "description": "AI-powered candidate screening and ranking for recruiters, hiring managers, ATS providers and recruitment AI agents. Ingests a job description and 1-50 candidate resumes, returning a ranked shortlist with score breakdowns across five weighted criteria: skills_match (tech stack and soft skills extracted from JD vs resume), experience_match (years vs seniority level inferred from JD), education_match (degree level + top-school detection), role_progression (Junior to Senior to Lead patterns), culture_fit_estimate (remote/hybrid, startup vs enterprise). Per candidate: overall_score 0-100, matched/missing skills, red_flags (job hopping, employment gaps, seniority mismatch), green_flags (long tenure, promotions), 3-5 interview questions, fit_summary. Diversity signals are first-name proxies ONLY with mandatory ethical WARNING. All processing is local -- no external API calls, instant response, privacy-preserving.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "candidates": { "description": "Array of candidate objects. Maximum 50.", "items": { "properties": { "id": { "description": "Unique identifier for this candidate.", "type": "string" }, "linkedin_url": { "description": "Optional LinkedIn URL (informational, not fetched).", "type": "string" }, "name": { "description": "Optional full name (used for diversity signals).", "type": "string" }, "resume_text": { "description": "Raw resume or CV text.", "minLength": 10, "type": "string" }, "years_experience": { "description": "Optional self-reported years of total experience.", "minimum": 0, "type": "number" } }, "required": [ "id", "resume_text" ], "type": "object" }, "maxItems": 50, "minItems": 1, "type": "array" }, "criteria_weights": { "description": "Optional weighting per criterion. Default: skills=0.4, experience=0.2, education=0.1, progression=0.15, culture=0.15.", "properties": { "culture_fit": { "maximum": 1, "minimum": 0, "type": "number" }, "education_match": { "maximum": 1, "minimum": 0, "type": "number" }, "experience_match": { "maximum": 1, "minimum": 0, "type": "number" }, "role_progression": { "maximum": 1, "minimum": 0, "type": "number" }, "skills_match": { "maximum": 1, "minimum": 0, "type": "number" } }, "type": "object" }, "job_description": { "description": "Full text or summary of the job description and role requirements.", "minLength": 20, "type": "string" }, "role_country": { "description": "Optional ISO 2-letter country code for regional context (informational).", "type": "string" } }, "required": [ "job_description", "candidates" ], "type": "object" }, "name": "candidate_screening_ranking", "outputSchema": { "properties": { "candidates_ranked": { "items": { "properties": { "fit_summary": { "type": "string" }, "green_flags": { "items": { "type": "string" }, "type": "array" }, "id": { "type": "string" }, "matched_skills": { "items": { "type": "string" }, "type": "array" }, "missing_skills": { "items": { "type": "string" }, "type": "array" }, "name": { "type": "string" }, "overall_score": { "type": "number" }, "recommended_interview_questions": { "items": { "type": "string" }, "type": "array" }, "red_flags": { "items": { "type": "string" }, "type": "array" }, "score_breakdown": { "properties": { "culture_fit_estimate": { "type": "number" }, "education_match": { "type": "number" }, "experience_match": { "type": "number" }, "role_progression": { "type": "number" }, "skills_match": { "type": "number" } }, "required": [ "skills_match", "experience_match", "education_match", "role_progression", "culture_fit_estimate" ], "type": "object" } }, "required": [ "id", "overall_score", "score_breakdown", "matched_skills", "missing_skills", "red_flags", "green_flags", "recommended_interview_questions", "fit_summary" ], "type": "object" }, "type": "array" }, "diversity_signals": { "properties": { "inferred_gender_balance_pct": { "type": "number" }, "risk_bias_alerts": { "items": { "type": "string" }, "type": "array" } }, "required": [ "risk_bias_alerts" ], "type": "object" }, "job_description_summary": { "type": "string" }, "nice_to_have": { "items": { "type": "string" }, "type": "array" }, "quality_score": { "type": "number" }, "required_skills": { "items": { "type": "string" }, "type": "array" }, "shortlist_recommended": { "items": { "type": "string" }, "type": "array" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "partial", "unavailable" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" } }, "required": [ "job_description_summary", "required_skills", "nice_to_have", "candidates_ranked", "shortlist_recommended", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Stratège du cap table — Gapup agent-payable C-suite expertise (FUNDRAISING). Returns a structured, audited deliverable. Reference case: Aleph AI Series B — modèle dilution multi-rounds + simulations secondaires + hygiène equity · 5 scenarios. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "arrEur": { "minimum": 0, "type": "number" }, "foundedYear": { "type": "integer" }, "name": { "type": "string" }, "sector": { "type": "string" } }, "required": [ "name", "sector", "foundedYear" ], "type": "object" }, "currentCapTable": { "properties": { "esopPoolUnallocatedShares": { "type": "number" }, "holders": { "items": { "properties": { "name": { "type": "string" }, "shares": { "type": "number" }, "type": { "enum": [ "founder", "co-founder", "early-employee", "esop-pool", "angel", "vc-seed", "vc-series-a", "vc-series-b", "vc-series-c", "vc-later", "advisor", "secondary-buyer" ], "type": "string" }, "vestingSchedule": { "type": "string" } }, "required": [ "name", "type", "shares" ], "type": "object" }, "maxItems": 20, "minItems": 3, "type": "array" }, "safeNotesOutstandingEur": { "minimum": 0, "type": "number" }, "totalSharesOutstanding": { "type": "number" } }, "required": [ "totalSharesOutstanding", "holders", "esopPoolUnallocatedShares" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" }, "founderObjectives": { "maxLength": 800, "minLength": 20, "type": "string" }, "plannedRounds": { "items": { "properties": { "expectedTimeline": { "type": "string" }, "roundType": { "enum": [ "seed", "series-a", "series-b", "series-c", "series-d-plus", "bridge", "secondary" ], "type": "string" }, "targetAmountEur": { "type": "number" }, "targetPreMoneyEur": { "minimum": 0, "type": "number" } }, "required": [ "roundType", "targetAmountEur", "targetPreMoneyEur", "expectedTimeline" ], "type": "object" }, "maxItems": 4, "minItems": 1, "type": "array" } }, "required": [ "company", "currentCapTable", "founderObjectives", "plannedRounds" ], "type": "object" }, "name": "cap_table_strategist", "outputSchema": null }, { "description": "Planification capacitaire — Gapup agent-payable C-suite expertise (CHRO). Returns a structured, audited deliverable. Reference case: Gapup Hub — 22→48 FTE en 12m · ARR €480k→€1.7M · Plan d'embauches par département. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "benchmarks": { "properties": { "arrPerEmployee": { "minimum": 0, "type": "number" }, "targetArrPerEmployee": { "minimum": 0, "type": "number" } }, "type": "object" }, "company": { "properties": { "name": { "maxLength": 100, "type": "string" }, "sector": { "maxLength": 100, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "constraints": { "items": { "maxLength": 200, "type": "string" }, "maxItems": 5, "type": "array" }, "currentTeam": { "properties": { "departments": { "items": { "properties": { "annualCostPerHeadEur": { "minimum": 0, "type": "number" }, "currentHeadcount": { "minimum": 0, "type": "integer" }, "name": { "maxLength": 100, "type": "string" }, "openPositions": { "minimum": 0, "type": "integer" }, "productivityMetric": { "maxLength": 100, "type": "string" } }, "required": [ "name", "currentHeadcount" ], "type": "object" }, "maxItems": 10, "minItems": 1, "type": "array" }, "totalHeadcount": { "minimum": 0, "type": "integer" } }, "required": [ "totalHeadcount", "departments" ], "type": "object" }, "financials": { "properties": { "currentArrEur": { "minimum": 0, "type": "number" }, "currentBurnEur": { "minimum": 0, "type": "number" }, "targetArrEur": { "minimum": 0, "type": "number" }, "timeframeMonths": { "maximum": 36, "minimum": 6, "type": "integer" } }, "required": [ "currentArrEur", "targetArrEur", "timeframeMonths" ], "type": "object" }, "hiringBudgetEur": { "minimum": 0, "type": "number" } }, "required": [ "company", "financials", "currentTeam" ], "type": "object" }, "name": "capacity_planning", "outputSchema": null }, { "description": "Stratégie de financement — Gapup agent-payable C-suite expertise (CSO). Returns a structured, audited deliverable. Reference case: Alan assurance santé SaaS — séquence Seed→A→B→C (2016-2022). Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "countryHQ": { "maxLength": 60, "minLength": 2, "type": "string" }, "foundedYear": { "maximum": 2030, "minimum": 1990, "type": "integer" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "pitch": { "maxLength": 800, "minLength": 20, "type": "string" }, "stage": { "enum": [ "pre-seed", "seed", "series-a", "series-b", "series-c", "growth", "public" ], "type": "string" }, "url": { "format": "uri", "type": "string" } }, "required": [ "name", "pitch", "stage", "countryHQ", "foundedYear" ], "type": "object" }, "financialPosition": { "properties": { "burnMonthlyEur": { "minimum": 0, "type": "number" }, "cashEur": { "minimum": 0, "type": "number" }, "cashRunwayMonths": { "minimum": 0, "type": "number" }, "grossMarginPct": { "maximum": 100, "minimum": 0, "type": "number" }, "hasJEIStatus": { "type": "boolean" }, "hasRDEligibility": { "type": "boolean" } }, "required": [ "cashRunwayMonths", "cashEur", "burnMonthlyEur", "grossMarginPct" ], "type": "object" }, "founderConstraints": { "properties": { "controlRetentionPriority": { "enum": [ "low", "medium", "high", "absolute" ], "type": "string" }, "maxDilutionFounderPct": { "maximum": 100, "minimum": 0, "type": "number" }, "mustAvoidInstruments": { "items": { "type": "string" }, "maxItems": 5, "type": "array" }, "preferredProfile": { "enum": [ "control-max", "growth-max", "risk-min" ], "type": "string" } }, "required": [ "maxDilutionFounderPct", "controlRetentionPriority" ], "type": "object" }, "growthPlan": { "properties": { "currentArrEur": { "minimum": 0, "type": "number" }, "headcountNow": { "minimum": 0, "type": "integer" }, "headcountTarget36m": { "minimum": 0, "type": "integer" }, "keyMilestones": { "items": { "maxLength": 200, "minLength": 10, "type": "string" }, "maxItems": 8, "minItems": 2, "type": "array" }, "targetArrEur36m": { "minimum": 0, "type": "number" } }, "required": [ "currentArrEur", "targetArrEur36m", "headcountNow", "headcountTarget36m", "keyMilestones" ], "type": "object" } }, "required": [ "company", "growthPlan", "financialPosition", "founderConstraints" ], "type": "object" }, "name": "capital_strategy", "outputSchema": null }, { "description": "Calculate a company's greenhouse-gas footprint under the GHG Protocol (Scope 1 + 2 + 3, in tCO2eq, tier-2 accuracy ±20%). Returns the emissions breakdown, hotspot identification, 5-8 reduction levers each with capex and payback, an SBTi-aligned reduction trajectory over 5-25 years, the 15 Scope-3 categories in detail, and CSRD/ESRS reporting readiness. When to use this tool: the user needs a carbon assessment for CSRD compliance pre-audit, green-finance access, or supplier ESG scorecards. Inputs: the company profile and its activity data. Delivered by Émilie, the AI Sustainability lead of the Gapup portfolio.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "fte": { "minimum": 1, "type": "integer" }, "locations": { "minimum": 1, "type": "integer" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "referenceYear": { "maximum": 2030, "minimum": 2018, "type": "integer" }, "revenueEur": { "minimum": 0, "type": "number" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "fte", "referenceYear" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" }, "perimeter": { "properties": { "excludedActivities": { "maxLength": 400, "type": "string" }, "geographic": { "items": { "type": "string" }, "maxItems": 20, "minItems": 1, "type": "array" }, "organizational": { "enum": [ "operational-control", "financial-control", "equity-share" ], "type": "string" } }, "required": [ "organizational", "geographic" ], "type": "object" }, "reductionTargets": { "properties": { "sbtiAligned": { "type": "boolean" }, "targetReductionPct": { "maximum": 100, "minimum": 0, "type": "number" }, "targetYear": { "maximum": 2050, "minimum": 2025, "type": "integer" } }, "required": [ "sbtiAligned", "targetYear", "targetReductionPct" ], "type": "object" }, "scope1Sources": { "items": { "properties": { "annualConsumption": { "minimum": 0, "type": "number" }, "fuelType": { "maxLength": 80, "minLength": 2, "type": "string" }, "source": { "maxLength": 120, "minLength": 2, "type": "string" }, "unit": { "maxLength": 40, "type": "string" } }, "required": [ "source", "fuelType", "annualConsumption", "unit" ], "type": "object" }, "maxItems": 15, "type": "array" }, "scope2Sources": { "items": { "properties": { "annualConsumptionKWh": { "minimum": 0, "type": "number" }, "energyType": { "enum": [ "electricity", "heat", "steam", "cooling" ], "type": "string" }, "isRenewableContract": { "type": "boolean" }, "location": { "maxLength": 80, "minLength": 2, "type": "string" }, "source": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "source", "energyType", "annualConsumptionKWh", "location", "isRenewableContract" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "scope3Activities": { "items": { "properties": { "category": { "description": "GHG Protocol Scope 3 category 1-15", "type": "string" }, "description": { "maxLength": 300, "minLength": 10, "type": "string" }, "estimatedScale": { "maxLength": 120, "minLength": 4, "type": "string" } }, "required": [ "category", "description" ], "type": "object" }, "maxItems": 15, "type": "array" } }, "required": [ "company", "perimeter", "scope2Sources" ], "type": "object" }, "name": "carbon_footprint_calculator", "outputSchema": { "properties": { "breakdown": { "description": "Emissions breakdown by scope", "properties": { "scope1Tco2eq": { "type": "number" }, "scope2Tco2eq": { "type": "number" }, "scope3Tco2eq": { "type": "number" } }, "required": [ "scope1Tco2eq", "scope2Tco2eq", "scope3Tco2eq" ], "type": "object" }, "csrdReadiness": { "description": "CSRD/ESRS reporting readiness assessment", "properties": { "gaps": { "items": { "type": "string" }, "type": "array" }, "nextSteps": { "items": { "type": "string" }, "type": "array" }, "overallScore": { "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "overallScore" ], "type": "object" }, "executiveSummary": { "description": "Board-ready GHG assessment prose", "type": "string" }, "hotspots": { "description": "Top emission sources ranked by contribution", "items": { "properties": { "emissionsTco2eq": { "type": "number" }, "scope": { "enum": [ "1", "2", "3" ], "type": "string" }, "shareOfTotal": { "description": "Percentage of total footprint", "type": "number" }, "source": { "type": "string" } }, "required": [ "source", "scope", "emissionsTco2eq" ], "type": "object" }, "type": "array" }, "kpis": { "description": "3-5 headline ESG KPI bubbles", "items": { "properties": { "label": { "type": "string" }, "trend": { "enum": [ "up", "down", "stable" ], "type": "string" }, "value": { "type": "string" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "reductionLevers": { "description": "5-8 actionable reduction levers with financial analysis", "items": { "properties": { "capexEur": { "type": "number" }, "lever": { "type": "string" }, "paybackYears": { "type": "number" }, "priority": { "enum": [ "high", "medium", "low" ], "type": "string" }, "reductionPotentialTco2eq": { "type": "number" } }, "required": [ "lever", "reductionPotentialTco2eq" ], "type": "object" }, "type": "array" }, "sbtiTrajectory": { "description": "SBTi-aligned annual reduction trajectory", "items": { "properties": { "reductionVsBaselinePct": { "type": "number" }, "targetEmissionsTco2eq": { "type": "number" }, "year": { "type": "integer" } }, "required": [ "year", "targetEmissionsTco2eq" ], "type": "object" }, "type": "array" }, "scope3Categories": { "description": "GHG Protocol 15 Scope-3 categories detail", "items": { "properties": { "category": { "type": "string" }, "categoryNumber": { "maximum": 15, "minimum": 1, "type": "integer" }, "dataQuality": { "enum": [ "measured", "estimated", "excluded" ], "type": "string" }, "emissionsTco2eq": { "type": "number" } }, "required": [ "category", "emissionsTco2eq" ], "type": "object" }, "type": "array" }, "totalEmissionsTco2eq": { "description": "Total GHG footprint in tCO2eq (Scope 1+2+3 combined, ±20% tier-2 accuracy)", "type": "number" } }, "required": [ "executiveSummary", "totalEmissionsTco2eq", "breakdown", "hotspots", "reductionLevers", "csrdReadiness" ], "type": "object" } }, { "description": "Roadmap carbone — Gapup agent-payable C-suite expertise (SUSTAINABILITY). Returns a structured, audited deliverable. Reference case: Cas démo — Roadmap carbone. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "fte": { "minimum": 1, "type": "integer" }, "locations": { "minimum": 1, "type": "integer" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "referenceYear": { "maximum": 2030, "minimum": 2018, "type": "integer" }, "revenueEur": { "minimum": 0, "type": "number" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "fte", "referenceYear" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" }, "perimeter": { "properties": { "excludedActivities": { "maxLength": 400, "type": "string" }, "geographic": { "items": { "type": "string" }, "maxItems": 20, "minItems": 1, "type": "array" }, "organizational": { "type": "string" } }, "required": [ "organizational", "geographic" ], "type": "object" }, "reductionTargets": { "properties": { "sbtiAligned": { "type": "boolean" }, "targetReductionPct": { "maximum": 100, "minimum": 0, "type": "number" }, "targetYear": { "maximum": 2050, "minimum": 2025, "type": "integer" } }, "required": [ "sbtiAligned", "targetYear", "targetReductionPct" ], "type": "object" }, "scope1Sources": { "items": { "properties": { "annualConsumption": { "minimum": 0, "type": "number" }, "fuelType": { "maxLength": 80, "minLength": 2, "type": "string" }, "source": { "maxLength": 120, "minLength": 2, "type": "string" }, "unit": { "maxLength": 40, "type": "string" } }, "required": [ "source", "fuelType", "annualConsumption", "unit" ], "type": "object" }, "maxItems": 15, "type": "array" }, "scope2Sources": { "items": { "properties": { "annualConsumptionKWh": { "minimum": 0, "type": "number" }, "energyType": { "type": "string" }, "isRenewableContract": { "type": "boolean" }, "location": { "maxLength": 80, "minLength": 2, "type": "string" }, "source": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "source", "energyType", "annualConsumptionKWh", "location", "isRenewableContract" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "scope3Activities": { "items": { "properties": { "category": { "enum": [ "purchased-goods-services", "capital-goods", "fuel-energy-related", "upstream-transportation", "waste-operations", "business-travel", "employee-commuting", "upstream-leased-assets", "downstream-transportation", "processing-sold", "use-sold-products", "end-of-life-sold", "downstream-leased-assets", "franchises", "investments" ], "type": "string" }, "description": { "maxLength": 300, "minLength": 10, "type": "string" }, "estimatedScale": { "maxLength": 120, "minLength": 4, "type": "string" } }, "required": [ "category", "description" ], "type": "object" }, "maxItems": 15, "type": "array" } }, "required": [ "company", "perimeter", "scope2Sources" ], "type": "object" }, "name": "carbon_roadmap", "outputSchema": null }, { "description": "Cartographie du champion — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Spendesk × Decathlon (deal €120k/an) — Champion identifié : CFO Group · Plan 6 semaines multi-touch. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "deal": { "properties": { "companyName": { "maxLength": 200, "minLength": 2, "type": "string" }, "dealSizeEur": { "minimum": 0, "type": "number" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" }, "stage": { "maxLength": 60, "minLength": 2, "type": "string" } }, "required": [ "companyName", "sector", "dealSizeEur", "stage" ], "type": "object" }, "knownContacts": { "items": { "properties": { "department": { "maxLength": 60, "minLength": 2, "type": "string" }, "engagementLevel": { "enum": [ "none", "low", "medium", "high" ], "type": "string" }, "name": { "maxLength": 100, "minLength": 2, "type": "string" }, "notes": { "maxLength": 300, "type": "string" }, "title": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "title", "department", "engagementLevel" ], "type": "object" }, "maxItems": 10, "minItems": 1, "type": "array" }, "sellerContext": { "properties": { "dealVelocityDays": { "minimum": 0, "type": "number" }, "mainValueProp": { "maxLength": 300, "minLength": 10, "type": "string" }, "ourProductName": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "ourProductName", "mainValueProp" ], "type": "object" } }, "required": [ "deal", "knownContacts", "sellerContext" ], "type": "object" }, "name": "champion_mapping", "outputSchema": null }, { "description": "Classifies root causes of change failures for CTO-level incident analysis. Uses GitHub PR metadata and Snyk vulnerability data to identify patterns like dependency vulnerabilities, configuration drift, or deployment process gaps. Inputs include GitHub PR URL or incident ID, and outputs structured root cause categories with confidence scores. Ideal for post-mortem analysis and change risk assessment.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "incident_id": { "type": "string" }, "pr_url": { "pattern": "^https://github.com/[^/]+/[^/]+/pull/\\d+$", "type": "string" }, "snyk_org_id": { "type": "string" }, "time_range_days": { "maximum": 90, "minimum": 1, "type": "number" } }, "required": [ "pr_url" ], "type": "object" }, "name": "change_failure_root_cause_classifier", "outputSchema": { "properties": { "root_causes": { "items": { "properties": { "category": { "enum": [ "dependency_vulnerability", "configuration_drift", "deployment_process", "code_logic", "infrastructure", "third_party_service" ], "type": "string" }, "confidence": { "maximum": 1, "minimum": 0, "type": "number" }, "evidence": { "items": { "type": "string" }, "type": "array" }, "suggested_actions": { "items": { "type": "string" }, "type": "array" } }, "required": [ "category", "confidence" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "source_type": { "enum": [ "github_pr", "snyk_vuln" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at", "source_type" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Chinese e-commerce intelligence for the ZH diaspora (50M+), import-export teams, brand IP enforcement, MENA/Africa entrepreneurs sourcing from China, and brand monitoring. Covers Taobao, Tmall, JD.com, Pinduoduo, 1688.com (B2B) and AliExpress (cross-border).\n\nFive modes:\n• product_search — search products by keyword across CN platforms. Returns title ZH/EN, price CNY + USD estimate, sales 30d, rating, seller info, product URL.\n• seller_profile — full seller/supplier dossier: factory vs reseller detection, certifications (ISO, BSCI, CE), rating, years in business, main categories.\n• price_history — 12-month price trend for a product (live current price + seasonal model for CN shopping festivals: 11.11, 6.18, CNY).\n• brand_monitoring — detect counterfeits and grey market listings: price anomaly detection (>50% below MSRP = suspicious), counterfeit keyword scan, risk score 0-100.\n• market_intel — category overview: top 5 sellers by market share, avg/median price, volume estimate, price range.\n\nData quality note: LIVE data from Taobao/Tmall/JD/Pinduoduo REQUIRES AICI_RESEARCH_PROXY_URL with CN residential routing (Bright Data -country-cn). Without proxy: AliExpress (cross-border) + curated category fallback available.\n\nInput formats for seller_profile: 'platform:id' e.g. 'aliexpress:123456', '1688:87654321', 'tmall:apple-store-official'.\nInput formats for price_history: AliExpress product URL or numeric product ID.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "mode": { "description": "Analysis mode. product_search=find products, seller_profile=supplier dossier, price_history=price trend, brand_monitoring=counterfeit detection, market_intel=category overview.", "enum": [ "product_search", "seller_profile", "price_history", "brand_monitoring", "market_intel" ], "type": "string" }, "platform": { "description": "Target platform. Default: all. Note: taobao/tmall/jd/pinduoduo require CN proxy.", "enum": [ "taobao", "tmall", "jd", "pinduoduo", "1688", "aliexpress", "all" ], "type": "string" }, "query": { "description": "Keyword, product name, product_id, seller_id (platform:id), brand name, or category. Accepts Chinese characters (ZH) or English.", "maxLength": 300, "minLength": 1, "type": "string" }, "region": { "description": "Market region. CN-domestic=full platform coverage, cross-border=AliExpress+1688 focus. Default: CN-domestic.", "enum": [ "CN-domestic", "cross-border" ], "type": "string" } }, "required": [ "mode", "query" ], "type": "object" }, "name": "china_ecommerce_intel", "outputSchema": { "properties": { "brand_monitoring": { "properties": { "brand": { "type": "string" }, "counterfeit_indicators": { "items": { "properties": { "image_mismatch": { "type": "boolean" }, "price_anomaly_pct": { "type": "number" }, "product_id": { "type": "string" }, "seller_unverified": { "type": "boolean" }, "title": { "type": "string" } }, "required": [ "product_id", "title", "price_anomaly_pct", "seller_unverified", "image_mismatch" ], "type": "object" }, "type": "array" }, "legitimate_listings": { "type": "number" }, "risk_score": { "type": "number" }, "suspicious_listings": { "type": "number" } }, "required": [ "brand", "legitimate_listings", "suspicious_listings", "counterfeit_indicators", "risk_score" ], "type": "object" }, "market_intel": { "properties": { "avg_price_rmb": { "type": "number" }, "category": { "type": "string" }, "median_price_rmb": { "type": "number" }, "monthly_volume_est": { "type": "number" }, "price_range": { "properties": { "max": { "type": "number" }, "min": { "type": "number" } }, "required": [ "min", "max" ], "type": "object" }, "top_sellers": { "items": { "properties": { "market_share_pct": { "type": "number" }, "name": { "type": "string" }, "total_revenue_rmb_est": { "type": "number" } }, "required": [ "name", "market_share_pct", "total_revenue_rmb_est" ], "type": "object" }, "type": "array" } }, "required": [ "category", "top_sellers", "avg_price_rmb", "median_price_rmb", "price_range", "monthly_volume_est" ], "type": "object" }, "mode": { "type": "string" }, "platform_used": { "items": { "type": "string" }, "type": "array" }, "price_history": { "items": { "properties": { "date": { "type": "string" }, "price_rmb": { "type": "number" }, "source": { "type": "string" } }, "required": [ "date", "price_rmb", "source" ], "type": "object" }, "type": "array" }, "products": { "items": { "properties": { "category": { "type": "string" }, "image_url": { "type": "string" }, "platform": { "type": "string" }, "price_rmb": { "type": "number" }, "price_usd_estimate": { "type": "number" }, "product_id": { "type": "string" }, "product_url": { "type": "string" }, "rating": { "type": "number" }, "review_count": { "type": "number" }, "sales_30d": { "type": "number" }, "seller_id": { "type": "string" }, "seller_name": { "type": "string" }, "title_en": { "type": "string" }, "title_zh": { "type": "string" } }, "required": [ "product_id", "title_zh", "seller_id", "seller_name", "price_rmb", "price_usd_estimate", "product_url", "platform" ], "type": "object" }, "type": "array" }, "quality_score": { "type": "number" }, "seller_profile": { "properties": { "certifications": { "items": { "type": "string" }, "type": "array" }, "factory_or_reseller": { "enum": [ "factory", "reseller", "unknown" ], "type": "string" }, "main_categories": { "items": { "type": "string" }, "type": "array" }, "name_zh": { "type": "string" }, "platform": { "type": "string" }, "products_count": { "type": "number" }, "rating": { "type": "number" }, "response_rate_pct": { "type": "number" }, "seller_id": { "type": "string" }, "years_in_business": { "type": "number" } }, "required": [ "seller_id", "name_zh", "platform", "factory_or_reseller" ], "type": "object" }, "signals": { "items": { "properties": { "category": { "type": "string" }, "message": { "type": "string" }, "severity": { "enum": [ "info", "warning", "critical" ], "type": "string" } }, "required": [ "severity", "category", "message" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "partial", "unavailable" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" } }, "required": [ "mode", "platform_used", "signals", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Chinese capital market intelligence for the ZH diaspora (50M+) and institutional investors. Covers A-Shares (SSE/SZSE), H-Shares (HKEX), and ADRs across four modes:\n\n• company — full company profile: name ZH/EN, USCC (18-digit social credit code), exchange, industry (CSRC classification), chairperson, registered capital, SOE flag\n• market_quote — real-time quote: price (CNY or HKD), change%, volume, market cap, P/E ratio, dividend yield, last update timestamp\n• sector_overview — sector snapshot: top 5 companies by market cap, avg P/E, 30-day sector index change. Supported sectors: semiconductor, ev, battery, technology, finance, energy, realestate, consumer, pharma, telecom\n• regulatory_filing — recent regulatory disclosures (HKEX filings: annual, quarterly, announcements, mergers, IPOs) with title, date, document URL\n\nInput formats accepted:\n • 6-digit A-Share ticker (e.g. '600519' for Moutai SSE)\n • HKEX ticker (e.g. '0700.HK' or '700' for Tencent)\n • Company name in EN or ZH (e.g. '腾讯', 'Kweichow Moutai')\n • Sector keyword (e.g. 'semiconductor', '半导体')\n\nData sources: Yahoo Finance (primary, always accessible), Eastmoney push2 + CompanySurvey (via Bright Data proxy when AICI_RESEARCH_PROXY_URL is set), HKEX filing API. Note: Eastmoney/CSRC/SSE are blocked from datacenter IPs without proxy — set AICI_RESEARCH_PROXY_URL to unlock full coverage.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "exchange": { "description": "Exchange filter. Default: all. Affects sector_overview ticker selection.", "enum": [ "SSE", "SZSE", "HKEX", "all" ], "type": "string" }, "mode": { "description": "Analysis mode. company=full profile, market_quote=price data, sector_overview=top 5 by sector, regulatory_filing=recent filings.", "enum": [ "company", "market_quote", "sector_overview", "regulatory_filing" ], "type": "string" }, "period_days": { "description": "Lookback period in days for regulatory filings. Default: 30.", "maximum": 365, "minimum": 1, "type": "number" }, "query": { "description": "Ticker (6-digit A-share, 4-digit HK, Yahoo format), company name (ZH or EN), or sector keyword.", "maxLength": 200, "minLength": 1, "type": "string" } }, "required": [ "mode", "query" ], "type": "object" }, "name": "china_market_data", "outputSchema": { "properties": { "company": { "properties": { "address": { "type": "string" }, "chairperson": { "type": "string" }, "exchange": { "enum": [ "SSE", "SZSE", "HKEX", "NYSE-ADR", "private" ], "type": "string" }, "industry_csrc": { "type": "string" }, "industry_eng": { "type": "string" }, "listed_date": { "type": "string" }, "name_en": { "type": "string" }, "name_zh": { "type": "string" }, "registered_capital_rmb": { "type": "number" }, "state_owned": { "type": "boolean" }, "ticker": { "type": "string" }, "uscc": { "type": "string" } }, "required": [ "name_zh" ], "type": "object" }, "market_quote": { "properties": { "change_pct": { "type": "number" }, "currency": { "type": "string" }, "dividend_yield": { "type": "number" }, "exchange": { "type": "string" }, "last_update": { "type": "string" }, "market_cap_rmb": { "type": "number" }, "pe_ratio": { "type": "number" }, "price_rmb_or_hkd": { "type": "number" }, "ticker": { "type": "string" }, "volume": { "type": "number" } }, "required": [ "ticker", "exchange", "price_rmb_or_hkd", "currency", "change_pct", "volume", "last_update" ], "type": "object" }, "mode": { "type": "string" }, "quality_score": { "type": "number" }, "query": { "type": "string" }, "regulatory_filings": { "items": { "properties": { "date": { "type": "string" }, "source": { "enum": [ "CSRC", "SSE", "SZSE", "HKEX" ], "type": "string" }, "title": { "type": "string" }, "type": { "enum": [ "annual", "quarterly", "announcement", "merger", "ipo" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "date", "type", "title", "url", "source" ], "type": "object" }, "type": "array" }, "sector_overview": { "properties": { "avg_pe": { "type": "number" }, "sector_en": { "type": "string" }, "sector_index_change_30d": { "type": "number" }, "sector_zh": { "type": "string" }, "top_5_companies": { "items": { "properties": { "market_cap": { "type": "number" }, "name": { "type": "string" }, "ticker": { "type": "string" } }, "required": [ "name", "ticker" ], "type": "object" }, "type": "array" } }, "required": [ "sector_zh", "sector_en", "top_5_companies" ], "type": "object" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "partial", "unavailable" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" } }, "required": [ "mode", "query", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Bouclier anti-churn — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Spendesk — portefeuille 400 clients PME/ETI, détection churn Q2 2025 (€8M ARR). Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "accounts": { "items": { "properties": { "arrEur": { "minimum": 0, "type": "number" }, "championsLeft": { "minimum": 0, "type": "integer" }, "competitorMentions": { "minimum": 0, "type": "integer" }, "contractRenewalDate": { "type": "string" }, "csmOwner": { "type": "string" }, "id": { "type": "string" }, "industry": { "type": "string" }, "lastLoginDaysAgo": { "minimum": 0, "type": "integer" }, "missedPayments": { "minimum": 0, "type": "integer" }, "name": { "type": "string" }, "notes": { "maxLength": 500, "type": "string" }, "npsScore": { "maximum": 100, "minimum": -100, "type": "number" }, "openTickets": { "minimum": 0, "type": "integer" }, "size": { "enum": [ "startup", "sme", "mid-market", "enterprise" ], "type": "string" }, "usageDropPct": { "maximum": 100, "minimum": -100, "type": "number" } }, "required": [ "id", "name", "arrEur", "contractRenewalDate", "industry", "size", "csmOwner" ], "type": "object" }, "maxItems": 30, "minItems": 5, "type": "array" }, "analysisWindowDays": { "default": 90, "maximum": 180, "minimum": 30, "type": "integer" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "arr": { "minimum": 0, "type": "number" }, "avgContractValueEur": { "minimum": 0, "type": "number" }, "currentChurnRatePct": { "maximum": 100, "minimum": 0, "type": "number" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "renewalWindowMonths": { "maximum": 12, "minimum": 1, "type": "integer" }, "sector": { "maxLength": 160, "minLength": 2, "type": "string" }, "targetChurnRatePct": { "maximum": 100, "minimum": 0, "type": "number" }, "totalAccounts": { "minimum": 1, "type": "integer" } }, "required": [ "name", "sector", "arr", "totalAccounts", "avgContractValueEur", "currentChurnRatePct", "targetChurnRatePct", "renewalWindowMonths" ], "type": "object" }, "csrContext": { "maxLength": 800, "type": "string" } }, "required": [ "company", "accounts", "analysisWindowDays" ], "type": "object" }, "name": "churn_defender", "outputSchema": null }, { "description": "Projections climatiques long terme par scénario IPCC (RCP AR5 + SSP AR6) pour toute localisation. Scénarios : RCP_4_5, RCP_8_5 (AR5), SSP1_2_6, SSP2_4_5, SSP3_7_0, SSP5_8_5 (AR6), ou 'all' (compare tous). Horizons : 2030–2100. Métriques : température (delta vs baseline 1990-2010, jours >35°C, nuits chaudes), précipitations (delta%, événements extrêmes, sécheresses), hausse du niveau de la mer (cm vs 2000), événements extrêmes (ouragans, inondations P100, sécheresses), indice incendie. Sorties : comparaison multi-scénarios, probabilité IPCC, signaux d'impact business par secteur. Sources : Open-Meteo CMIP6 (keyless), IPCC AR6 Atlas lookup, NOAA SLR projections. Usages : TCFD/CSRD physical risk, due diligence actifs long terme, assurance catastrophe, planification infrastructure. Cache 7j. SLA ≤20s.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "compare_baseline": { "description": "Comparer vs baseline 1990-2010 (défaut true)", "type": "boolean" }, "horizon_year": { "description": "Année horizon de la projection (2030–2100)", "maximum": 2100, "minimum": 2030, "type": "number" }, "location": { "description": "Localisation : {city, country?} ou {lat, lon}", "properties": { "city": { "description": "Ville (ex. 'Miami', 'Paris', 'Dhaka')", "type": "string" }, "country": { "description": "Pays ou code ISO (ex. 'France', 'US', 'BD')", "type": "string" }, "lat": { "description": "Latitude décimale", "type": "number" }, "lon": { "description": "Longitude décimale", "type": "number" } }, "type": "object" }, "metrics": { "description": "Métriques à inclure. Défaut : toutes.", "items": { "enum": [ "temperature", "precipitation", "sea_level", "extreme_events", "wildfire_index" ], "type": "string" }, "type": "array" }, "scenario": { "description": "Scénario IPCC. 'all' génère une comparaison multi-scénarios.", "enum": [ "RCP_4_5", "RCP_8_5", "SSP1_2_6", "SSP2_4_5", "SSP3_7_0", "SSP5_8_5", "all" ], "type": "string" } }, "required": [ "location", "scenario", "horizon_year" ], "type": "object" }, "name": "climate_scenario_rcp", "outputSchema": { "properties": { "baseline_period": { "type": "string" }, "business_impact_signals": { "items": { "type": "object" }, "type": "array" }, "horizon_year": { "type": "number" }, "ipcc_likelihood_label": { "type": "string" }, "location": { "properties": { "country": { "type": "string" }, "ipcc_region": { "type": "string" }, "lat": { "type": "number" }, "lon": { "type": "number" }, "resolved_name": { "type": "string" } }, "required": [ "resolved_name", "lat", "lon" ], "type": "object" }, "multi_scenario_comparison": { "items": { "type": "object" }, "type": "array" }, "projections": { "properties": { "extreme_events": { "type": "object" }, "precipitation": { "properties": { "delta_vs_baseline_pct": { "type": "number" }, "dry_spells_max_days": { "type": "number" }, "extreme_events_per_year": { "type": "number" }, "mean_annual_mm": { "type": "number" } }, "required": [ "mean_annual_mm", "delta_vs_baseline_pct", "extreme_events_per_year", "dry_spells_max_days" ], "type": "object" }, "sea_level": { "properties": { "coastal_flood_risk_pct": { "type": "number" }, "rise_cm_vs_2000": { "type": "number" } }, "type": "object" }, "temperature": { "properties": { "days_over_35c_per_year": { "type": "number" }, "delta_vs_baseline_c": { "type": "number" }, "hot_nights_per_year": { "type": "number" }, "mean_annual_c": { "type": "number" } }, "required": [ "mean_annual_c", "delta_vs_baseline_c", "days_over_35c_per_year", "hot_nights_per_year" ], "type": "object" }, "wildfire_index": { "type": "object" } }, "required": [ "temperature", "precipitation", "extreme_events" ], "type": "object" }, "quality_score": { "type": "number" }, "scenario": { "type": "string" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" } }, "required": [ "location", "scenario", "horizon_year", "projections", "ipcc_likelihood_label", "business_impact_signals", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Brief évidence clinique (GRADE) — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Answers: Review the clinical evidence for <drug/intervention> in <indication> — GRADE rating, key trials, safety signals. · Scan safety signals for <molecule> in <population> — adverse events, severity, frequency from FAERS and trial data. · Assess comparative effectiveness of <intervention> versus <comparator> for <disease> — what does the evidence show? · Is there evidence supporting drug repurposing of <molecule> for <new indication> — existing trials and GRADE quality? · What are the evidence gaps for <intervention> in <patient population> before formulary adoption? Reference case: Semaglutide 2.4mg · Chronic weight management in non-diabetic adults · GRADE high efficacy · studies found · nausea/GI signals · FDA approved · PubMed+ClinicalTrials+OpenFDA. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "date_range_years": { "default": 5, "maximum": 20, "minimum": 1, "type": "integer" }, "evidence_focus": { "default": "all", "enum": [ "efficacy", "safety", "adverse-events", "comparative-effectiveness", "all" ], "type": "string" }, "intervention": { "maxLength": 200, "type": "string" }, "intervention_type": { "enum": [ "drug", "device", "biologic", "procedure", "behavioral", "diagnostic" ], "type": "string" }, "max_studies": { "default": 20, "maximum": 50, "minimum": 5, "type": "integer" }, "target_disease": { "maxLength": 200, "type": "string" }, "topic": { "maxLength": 300, "minLength": 3, "type": "string" } }, "required": [ "topic", "max_studies", "evidence_focus", "date_range_years" ], "type": "object" }, "name": "clinical_evidence_briefer", "outputSchema": null }, { "description": "Clinical and pharmaceutical intelligence for biotech analysts, healthcare fund managers, pharma BD teams, catalyst-driven hedge funds and health journalists. Aggregates live data across five modes:\n• trials — active/completed clinical trials (ClinicalTrials.gov v2 + EU CTR in parallel, 450k+ records)\n• pipeline — full pipeline by sponsor: trial count by phase + top indications\n• approvals — FDA drug label approvals + mechanism of action (OpenFDA)\n• recalls — FDA enforcement recalls classified by severity (Class I/II/III)\n• adverse_events — FAERS aggregated reactions: top 10 reactions + serious% \n\nSignal detection (P0/P1/P2):\n P0 if Class I recall OR trial terminated for safety reason\n P1 if serious adverse events >30% OR ≥3 recalls in 12 months\n P2 otherwise (standard monitoring)\n\nAll sources are public and keyless. Optional env OPENFDA_API_KEY raises daily quota from 1,000 to 120,000 requests. SLA: ≤16s p95 (parallel fetch, 8s budget per source). Cache: 6h trials, 24h approvals, 12h recalls, 6h adverse events.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "country": { "description": "ISO 2-letter country code to filter trial sites (e.g. US, FR, DE).", "maxLength": 5, "type": "string" }, "max_results": { "description": "Maximum number of results to return. Default 20.", "maximum": 50, "minimum": 5, "type": "number" }, "mode": { "description": "Analysis mode. Default \"trials\". trials=clinical trials, pipeline=sponsor overview, approvals=FDA approvals, recalls=enforcement, adverse_events=FAERS", "enum": [ "trials", "pipeline", "approvals", "recalls", "adverse_events" ], "type": "string" }, "phase": { "description": "Filter trials by phase (1/2/3/4/NA). Only applies to modes trials and pipeline.", "enum": [ "1", "2", "3", "4", "NA" ], "type": "string" }, "query": { "description": "Drug name, indication, sponsor or molecule (e.g. \"atezolizumab\", \"metastatic NSCLC\", \"Roche\", \"semaglutide\")", "maxLength": 200, "minLength": 2, "type": "string" }, "status_filter": { "description": "Filter trials by status. Only applies to modes trials and pipeline.", "enum": [ "recruiting", "active", "completed", "terminated" ], "type": "string" } }, "required": [ "query" ], "type": "object" }, "name": "clinical_pharma_intel", "outputSchema": { "properties": { "adverse_events": { "properties": { "drug": { "type": "string" }, "serious_pct": { "type": "number" }, "source": { "enum": [ "FAERS" ], "type": "string" }, "top_reactions": { "items": { "properties": { "count": { "type": "number" }, "reaction": { "type": "string" } }, "required": [ "reaction", "count" ], "type": "object" }, "type": "array" }, "total_reports": { "type": "number" } }, "required": [ "drug", "total_reports", "top_reactions", "serious_pct", "source" ], "type": "object" }, "approvals": { "items": { "properties": { "drug": { "type": "string" }, "ema_date": { "type": "string" }, "fda_date": { "type": "string" }, "indication": { "type": "string" }, "mechanism": { "type": "string" }, "source": { "type": "string" }, "sponsor": { "type": "string" } }, "required": [ "drug", "sponsor", "indication", "source" ], "type": "object" }, "type": "array" }, "mode": { "type": "string" }, "pipeline": { "properties": { "by_phase": { "additionalProperties": { "type": "number" }, "type": "object" }, "sponsor": { "type": "string" }, "top_indications": { "items": { "properties": { "count": { "type": "number" }, "indication": { "type": "string" } }, "required": [ "indication", "count" ], "type": "object" }, "type": "array" }, "total_active_trials": { "type": "number" } }, "required": [ "sponsor", "total_active_trials", "by_phase", "top_indications" ], "type": "object" }, "quality_score": { "type": "number" }, "query": { "type": "string" }, "recalls": { "items": { "properties": { "affected_lots": { "type": "string" }, "classification": { "type": "string" }, "drug": { "type": "string" }, "reason": { "type": "string" }, "recall_date": { "type": "string" }, "source": { "enum": [ "FDA", "EMA" ], "type": "string" } }, "required": [ "drug", "recall_date", "classification", "reason", "source" ], "type": "object" }, "type": "array" }, "signals": { "items": { "properties": { "evidence": { "items": { "type": "string" }, "type": "array" }, "severity": { "enum": [ "P0", "P1", "P2" ], "type": "string" }, "summary": { "type": "string" } }, "required": [ "severity", "summary", "evidence" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "unavailable", "partial" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "trials": { "items": { "properties": { "completion_date": { "type": "string" }, "conditions": { "items": { "type": "string" }, "type": "array" }, "enrollment_target": { "type": "number" }, "euctr_id": { "type": "string" }, "interventions": { "items": { "type": "string" }, "type": "array" }, "last_update": { "type": "string" }, "nct_id": { "type": "string" }, "phase": { "type": "string" }, "primary_outcome": { "type": "string" }, "sites_countries": { "items": { "type": "string" }, "type": "array" }, "source": { "enum": [ "ClinicalTrials_gov", "EU_CTR", "WHO_ICTRP", "JPRN", "ChiCTR" ], "type": "string" }, "sponsor": { "type": "string" }, "start_date": { "type": "string" }, "status": { "type": "string" }, "title": { "type": "string" }, "url": { "type": "string" } }, "required": [ "title", "sponsor", "phase", "status", "conditions", "interventions", "sites_countries", "url", "source" ], "type": "object" }, "type": "array" } }, "required": [ "query", "mode", "signals", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Analyzes AWS and Azure cloud pricing data alongside RIPE regional demand trends to generate Reserved Instance purchase recommendations for CTOs. Inputs include target cloud provider, instance family, region, and desired commitment term. Outputs include cost savings percentage, optimal RI quantity, and regional demand insights. Ideal for reducing cloud spend with data-driven decisions. Keywords: cloud cost optimization, reserved instances, AWS pricing, Azure pricing, RIPE demand trends.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "cloud_provider": { "enum": [ "aws", "azure" ], "type": "string" }, "instance_family": { "type": "string" }, "region": { "type": "string" }, "term": { "enum": [ "1-year", "3-year" ], "type": "string" }, "utilization": { "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "cloud_provider", "instance_family", "region" ], "type": "object" }, "name": "cloud_cost_ri_optimizer", "outputSchema": { "properties": { "break_even_months": { "type": "number" }, "cost_savings_percentage": { "type": "number" }, "on_demand_cost": { "type": "number" }, "recommended_ri_quantity": { "type": "number" }, "regional_demand_score": { "maximum": 100, "minimum": 0, "type": "number" }, "ri_cost": { "type": "number" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "As a CTO, this tool analyzes your team's historical DORA metrics (deployment frequency, lead time, MTTR, change failure rate) and GitHub pull request data to recommend an optimal code review depth. Input your repository identifier and time range, and receive a structured recommendation on review rigor (light, standard, thorough) with supporting metrics and risk-adjusted rationale.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "repository": { "description": "GitHub repository identifier in format owner/repo", "type": "string" }, "riskTolerance": { "description": "Organization's risk tolerance level", "enum": [ "low", "medium", "high" ], "type": "string" }, "teamSize": { "description": "Number of active developers in the team", "minimum": 1, "type": "number" }, "timeRangeDays": { "description": "Number of days of historical data to analyze", "maximum": 365, "minimum": 7, "type": "number" } }, "required": [ "repository", "timeRangeDays" ], "type": "object" }, "name": "code_review_depth_optimizer", "outputSchema": { "properties": { "recommendation": { "properties": { "confidence": { "maximum": 1, "minimum": 0, "type": "number" }, "metrics": { "properties": { "changeFailureRate": { "type": "number" }, "deploymentFrequency": { "type": "number" }, "leadTime": { "type": "number" }, "mttr": { "type": "number" } }, "required": [ "changeFailureRate", "deploymentFrequency" ], "type": "object" }, "rationale": { "type": "string" }, "reviewDepth": { "enum": [ "light", "standard", "thorough" ], "type": "string" } }, "required": [ "reviewDepth", "confidence", "rationale", "metrics" ], "type": "object" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Compares local compensation benchmarks against HQ standards for CHROs, adjusting for cost-of-living and tax differentials. Inputs include job role, local and HQ locations, and salary range. Outputs include adjusted benchmark delta, cost-of-living multiplier, and tax impact. Keywords: compensation benchmark, geographic pay equity, cost-of-living adjustment, tax differential analysis.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "baseSalary": { "description": "Current base salary in local currency", "type": "number" }, "currency": { "description": "ISO 4217 currency code (e.g., 'USD')", "type": "string" }, "hqLocation": { "description": "HQ location (ISO 3166-2 code or city, country)", "type": "string" }, "jobRole": { "description": "Standardized job role (e.g., 'Software Engineer III')", "type": "string" }, "localLocation": { "description": "Local work location (ISO 3166-2 code or city, country)", "type": "string" } }, "required": [ "jobRole", "localLocation", "hqLocation" ], "type": "object" }, "name": "comp_benchmark_geo_delta", "outputSchema": { "properties": { "adjustedSalary": { "description": "Salary adjusted for cost-of-living and taxes", "type": "number" }, "benchmarkDelta": { "description": "Percentage difference between local and HQ benchmark", "type": "number" }, "confidenceScore": { "description": "0-1 confidence in data quality", "type": "number" }, "costOfLivingMultiplier": { "description": "Local cost-of-living index relative to HQ", "type": "number" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "taxImpact": { "description": "Estimated tax differential percentage", "type": "number" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Architecture plan de commissionnement — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Gapup Hub — Comp Plan 8 rôles commerciaux · OTE €65-280k · Budget comp €2.1M · Quota coverage 3.2×. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" }, "stage": { "enum": [ "pre-seed", "seed", "serie-a", "serie-b", "serie-c", "serie-d-plus", "profitable-sme", "enterprise" ], "type": "string" } }, "required": [ "name", "sector", "stage" ], "type": "object" }, "currentChallenges": { "items": { "maxLength": 200, "minLength": 5, "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" }, "geography": { "maxLength": 100, "minLength": 2, "type": "string" }, "preferredStructure": { "enum": [ "base-plus-commission", "ote-split", "revenue-share", "milestone-bonus", "hybrid" ], "type": "string" }, "salesTeam": { "properties": { "roles": { "items": { "properties": { "currentBaseSalary": { "minimum": 0, "type": "number" }, "currentOteSalary": { "minimum": 0, "type": "number" }, "headcount": { "maximum": 500, "minimum": 1, "type": "number" }, "role": { "enum": [ "ae", "sdr", "csm", "se", "manager", "vp" ], "type": "string" } }, "required": [ "role", "headcount" ], "type": "object" }, "maxItems": 8, "minItems": 1, "type": "array" }, "totalSalesHeadcount": { "maximum": 1000, "minimum": 1, "type": "number" } }, "required": [ "roles", "totalSalesHeadcount" ], "type": "object" }, "targets": { "properties": { "annualRevenueTargetEur": { "minimum": 0, "type": "number" }, "averageContractValueEur": { "minimum": 0, "type": "number" }, "churnTargetPct": { "maximum": 100, "minimum": 0, "type": "number" }, "salesCycleTargetDays": { "maximum": 730, "minimum": 1, "type": "number" } }, "required": [ "annualRevenueTargetEur", "averageContractValueEur", "salesCycleTargetDays" ], "type": "object" } }, "required": [ "company", "salesTeam", "targets", "currentChallenges" ], "type": "object" }, "name": "comp_plan_architect", "outputSchema": null }, { "description": "Gold-standard competitive deep dive — STRUCTURED multi-source data (no LLM narrative). Pair tool: `competitor_intel` for LLM-narrated board briefing + slide script. Aggregates Wikipedia, Yahoo Finance, SEC EDGAR, Wayback Machine, DuckDuckGo, HackerNews, domain scraping — all keyless. Returns agent-shaped JSON: KPIs (funding, employees, revenue, market cap), P0/P1/P2 competitive signals, pricing radar, competitor comparison matrix, Wayback timeline, positioning (sector/industry/icp_hypothesis/moat_signals), quality score. Every field is sourced or marked unavailable — no hallucinated figures. SLA: p50 ~25s, p95 ~30s · score 80+ on listed targets (US/EU/foreign) · score ~40 on private companies (no EDGAR/Yahoo data). Use sync for batch agents (≤30s tolerance). Use `competitive_deep_dive_async` + `competitive_deep_dive_result(job_id)` for conversational agents. Inputs: company name or domain (required), optional competitor list (≤5), optional depth (easy/medium/hard).", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "description": "Name or domain of the target company (e.g. 'Salesforce', 'notion.so', 'HubSpot CRM')", "maxLength": 200, "minLength": 2, "type": "string" }, "competitors": { "description": "Optional list of competitor names or domains to include in the comparison matrix (max 5)", "items": { "maxLength": 200, "type": "string" }, "maxItems": 5, "type": "array" }, "depth": { "description": "Research depth: 'easy' = Wikipedia + DDG (fast, ~15s); 'medium' = + Yahoo Finance + EDGAR + Wayback (default, ~45s); 'hard' = + HackerNews + domain surfaces + competitor deep dive (~120s)", "enum": [ "easy", "medium", "hard" ], "type": "string" } }, "required": [ "company" ], "type": "object" }, "name": "competitive_deep_dive", "outputSchema": { "properties": { "company": { "type": "string" }, "comparison": { "description": "Feature/dimension comparison between target and each competitor", "items": { "properties": { "competitors": { "items": { "properties": { "name": { "type": "string" }, "value": { "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" }, "dimension": { "type": "string" }, "target_value": { "type": "string" } }, "required": [ "dimension", "target_value", "competitors" ], "type": "object" }, "type": "array" }, "depth_used": { "enum": [ "easy", "medium", "hard" ], "type": "string" }, "domain_resolved": { "type": [ "string", "null" ] }, "generated_at": { "type": "string" }, "kpis": { "description": "Key Performance Indicators sourced from public data", "items": { "properties": { "label": { "type": "string" }, "source": { "type": "string" }, "sub": { "description": "Sub-label or context", "type": "string" }, "value": { "type": "string" } }, "required": [ "label", "value", "sub", "source" ], "type": "object" }, "type": "array" }, "positioning": { "description": "Positioning analysis derived from public data", "properties": { "icp_hypothesis": { "description": "Inferred Ideal Customer Profile based on website/Wikipedia", "type": "string" }, "industry": { "type": [ "string", "null" ] }, "moat_signals": { "description": "Observed moat indicators (brand, distribution, data network effect, etc.)", "items": { "type": "string" }, "type": "array" }, "sector": { "type": [ "string", "null" ] }, "summary": { "type": "string" } }, "required": [ "summary", "icp_hypothesis", "moat_signals" ], "type": "object" }, "pricing_radar": { "description": "Pricing tiers extracted from public sources", "items": { "properties": { "features": { "items": { "type": "string" }, "type": "array" }, "price": { "type": "string" }, "source_url": { "type": "string" }, "tier": { "type": "string" } }, "required": [ "tier", "price", "features", "source_url" ], "type": "object" }, "type": "array" }, "quality": { "properties": { "details": { "items": { "type": "string" }, "type": "array" }, "score": { "type": "number" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "weak_zones": { "items": { "type": "string" }, "type": "array" } }, "required": [ "score", "status", "weak_zones", "details" ], "type": "object" }, "signals": { "description": "Competitive intelligence signals, severity-ranked P0 (critical) to P2 (informational)", "items": { "properties": { "category": { "type": "string" }, "evidence": { "type": "string" }, "severity": { "enum": [ "P0", "P1", "P2" ], "type": "string" }, "source_url": { "type": "string" }, "summary": { "type": "string" } }, "required": [ "severity", "category", "summary", "evidence", "source_url" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "label": { "type": "string" }, "status": { "enum": [ "retrieved", "unavailable", "partial" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "label", "url", "status" ], "type": "object" }, "type": "array" }, "wayback_timeline": { "description": "Historical snapshots of the company website from Wayback Machine", "items": { "properties": { "date": { "type": "string" }, "note": { "type": "string" }, "snapshot_url": { "type": "string" } }, "required": [ "date", "snapshot_url" ], "type": "object" }, "type": "array" } }, "required": [ "company", "domain_resolved", "depth_used", "generated_at", "kpis", "signals", "pricing_radar", "comparison", "wayback_timeline", "positioning", "quality", "sources" ], "type": "object" } }, { "description": "Async variant of competitive_deep_dive. Returns immediately (<200ms) with a job_id. The research runs in the background (p50≈25s, p95≈30s for depth=medium). Poll the result with competitive_deep_dive_result(job_id) after the eta_seconds hint. Use this instead of competitive_deep_dive when the agent cannot wait >15s for a response. Inputs: same as competitive_deep_dive — company (required), competitors (optional list, max 5), depth (easy/medium/hard, default medium). Async tool — register a webhook via `webhooks_manage(register, url, [job.completed])` to receive callbacks instead of polling. Faster + lighter.", "inputSchema": { "properties": { "company": { "description": "Name or domain of the target company (e.g. 'Salesforce', 'notion.so')", "maxLength": 200, "minLength": 2, "type": "string" }, "competitors": { "description": "Optional list of competitor names or domains to include in the comparison matrix (max 5)", "items": { "maxLength": 200, "type": "string" }, "maxItems": 5, "type": "array" }, "depth": { "description": "Research depth: 'easy'≈15s, 'medium'≈30s (default), 'hard'≈60s", "enum": [ "easy", "medium", "hard" ], "type": "string" } }, "required": [ "company" ], "type": "object" }, "name": "competitive_deep_dive_async", "outputSchema": { "properties": { "eta_seconds": { "description": "Estimated seconds until result is ready", "type": "number" }, "job_id": { "description": "Unique job identifier — pass to competitive_deep_dive_result", "type": "string" }, "status": { "description": "Always 'queued' on submission", "enum": [ "queued" ], "type": "string" }, "submitted_at": { "description": "ISO-8601 submission timestamp", "type": "string" } }, "required": [ "job_id", "status", "eta_seconds", "submitted_at" ], "type": "object" } }, { "description": "Poll the result of a competitive_deep_dive_async job. Returns status=pending while running, status=completed with the full report once done, status=failed on error, or status=not_found if the job_id is unknown or expired (TTL 24h). Call this after the eta_seconds hint returned by competitive_deep_dive_async.", "inputSchema": { "properties": { "job_id": { "description": "The job_id returned by competitive_deep_dive_async", "type": "string" } }, "required": [ "job_id" ], "type": "object" }, "name": "competitive_deep_dive_result", "outputSchema": { "oneOf": [ { "properties": { "elapsed_seconds": { "type": "number" }, "eta_seconds": { "type": [ "number", "null" ] }, "status": { "enum": [ "pending" ], "type": "string" } }, "required": [ "status", "elapsed_seconds" ], "title": "Pending" }, { "properties": { "result": { "type": "object" }, "status": { "enum": [ "completed" ], "type": "string" }, "took_seconds": { "type": "number" } }, "required": [ "status", "result", "took_seconds" ], "title": "Completed" }, { "properties": { "error": { "type": "string" }, "status": { "enum": [ "failed" ], "type": "string" }, "took_seconds": { "type": "number" } }, "required": [ "status", "error", "took_seconds" ], "title": "Failed" }, { "properties": { "status": { "enum": [ "not_found" ], "type": "string" } }, "required": [ "status" ], "title": "NotFound" } ], "type": "object" } }, { "description": "LLM-narrated competitive-intelligence BRIEFING — for human consumption (board meeting, pitch prep). Pair tool: `competitive_deep_dive` for raw structured multi-source data (agent-shaped JSON). Returns: recent competitor moves with severity (critical/high/medium/low), prioritised signals, pricing-radar comparison, 3-6 quantified recommendations (impact in € or %, 7/30/90/180-day horizons), and an 8-12 slide presenter script. Use when the buyer wants a narrative briefing or a deck. Inputs: your company (name + one-paragraph pitch) + 1-10 competitors. Delivered by Manue, AI CMO of the Gapup portfolio.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "competitors": { "description": "1-10 competitors to analyze", "items": { "properties": { "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "url": { "format": "uri", "type": "string" } }, "required": [ "name", "url" ], "type": "object" }, "maxItems": 10, "minItems": 1, "type": "array" }, "focus": { "description": "Optional — what the buyer wants to track first (e.g. pricing moves, hiring patterns)", "maxLength": 400, "type": "string" }, "selfCompany": { "description": "Your company info", "properties": { "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "pitch": { "description": "One-paragraph pitch", "maxLength": 400, "minLength": 10, "type": "string" }, "url": { "format": "uri", "type": "string" } }, "required": [ "name", "pitch" ], "type": "object" } }, "required": [ "selfCompany", "competitors" ], "type": "object" }, "name": "competitor_intel", "outputSchema": { "properties": { "competitorMoves": { "description": "Recent moves per competitor with severity rating", "items": { "properties": { "competitor": { "type": "string" }, "date": { "type": "string" }, "move": { "type": "string" }, "severity": { "enum": [ "critical", "high", "medium", "low" ], "type": "string" }, "source": { "type": "string" } }, "required": [ "competitor", "move", "severity" ], "type": "object" }, "type": "array" }, "executiveSummary": { "description": "Board-ready prose summary (120-400 chars)", "type": "string" }, "kpis": { "description": "3-5 headline KPI bubbles", "items": { "properties": { "label": { "type": "string" }, "trend": { "enum": [ "up", "down", "stable" ], "type": "string" }, "value": { "type": "string" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "presenterScript": { "description": "8-12 slide board presenter script", "items": { "properties": { "keyPoints": { "items": { "type": "string" }, "type": "array" }, "slide": { "type": "integer" }, "speakerNote": { "type": "string" }, "title": { "type": "string" }, "visualHint": { "type": "string" } }, "required": [ "slide", "title", "keyPoints", "speakerNote" ], "type": "object" }, "type": "array" }, "pricingRadar": { "description": "Pricing comparison across competitors", "items": { "properties": { "competitor": { "type": "string" }, "features": { "items": { "type": "string" }, "type": "array" }, "priceEur": { "type": "number" }, "tier": { "type": "string" } }, "required": [ "competitor" ], "type": "object" }, "type": "array" }, "recommendations": { "description": "3-6 actionable strategic recommendations", "items": { "properties": { "action": { "type": "string" }, "expectedImpact": { "type": "string" }, "horizon": { "enum": [ "7d", "30d", "90d", "180d" ], "type": "string" }, "priority": { "enum": [ "critical", "high", "medium", "low" ], "type": "string" } }, "required": [ "action", "expectedImpact", "horizon" ], "type": "object" }, "type": "array" }, "sources": { "description": "Cited sources", "items": { "properties": { "excerpt": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url" ], "type": "object" }, "type": "array" } }, "required": [ "executiveSummary", "competitorMoves", "recommendations", "presenterScript" ], "type": "object" } }, { "description": "Mouvements concurrents — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Answers: What have my named competitors done recently — releases, pricing changes, hires, funding? · Which competitor signals are the most urgent right now and what should I do about them? Reference case: Notion — moves de ClickUp, Asana, Coda. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "competitors": { "items": { "properties": { "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "url": { "format": "uri", "type": "string" } }, "required": [ "name", "url" ], "type": "object" }, "maxItems": 10, "minItems": 1, "type": "array" }, "focus": { "maxLength": 400, "type": "string" }, "selfCompany": { "properties": { "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "pitch": { "maxLength": 400, "minLength": 10, "type": "string" }, "url": { "format": "uri", "type": "string" } }, "required": [ "name", "pitch" ], "type": "object" } }, "required": [ "selfCompany", "competitors" ], "type": "object" }, "name": "competitor_moves", "outputSchema": null }, { "description": "Radar pricing concurrents — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Answers: How do my competitors' pricing plans and monthly prices compare to mine? · Which competitor plan undercuts or out-features my equivalent tier? Reference case: Notion — pricing vs ClickUp, Asana, Coda. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "competitors": { "items": { "properties": { "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "url": { "format": "uri", "type": "string" } }, "required": [ "name", "url" ], "type": "object" }, "maxItems": 10, "minItems": 1, "type": "array" }, "focus": { "maxLength": 400, "type": "string" }, "selfCompany": { "properties": { "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "pitch": { "maxLength": 400, "minLength": 10, "type": "string" }, "url": { "format": "uri", "type": "string" } }, "required": [ "name", "pitch" ], "type": "object" } }, "required": [ "selfCompany", "competitors" ], "type": "object" }, "name": "competitor_pricing_radar", "outputSchema": null }, { "description": "Scrape and parse a competitor pricing page from a URL or domain. Fetches via proxy-aware timedFetch (tries /pricing, /plans, homepage fallback), then extracts: plan names, prices, billing cadence (monthly/annual/usage-based/one-time), key features, free tier presence, enterprise tier, estimated price range. Returns structured pricing tiers. If unfetchable or no pricing found (anti-bot, SPA, auth wall): returns a clear degraded result with warnings and signals — never fake success. ICP: founders, product managers, pricing strategists, competitive intel teams. Proxy-aware (AICI_RESEARCH_PROXY_URL). Cache TTL 6h.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "url": { "description": "Competitor URL or domain (e.g. 'https://notion.so/pricing', 'notion.so', 'https://www.example.com'). For best results, provide the direct pricing page URL.", "type": "string" } }, "required": [ "url" ], "type": "object" }, "name": "competitor_pricing_scrape", "outputSchema": { "properties": { "billing_model_signals": { "items": { "type": "string" }, "type": "array" }, "domain": { "type": "string" }, "estimated_price_range": { "type": "object" }, "has_enterprise_tier": { "type": "boolean" }, "has_free_tier": { "type": "boolean" }, "plan_names_detected": { "items": { "type": "string" }, "type": "array" }, "pricing_found": { "type": "boolean" }, "quality_score": { "type": "number" }, "raw_price_signals": { "items": { "type": "string" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "tiers": { "type": "array" }, "url_fetched": { "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "url_fetched", "domain", "pricing_found", "tiers", "raw_price_signals", "plan_names_detected", "has_free_tier", "has_enterprise_tier", "estimated_price_range", "billing_model_signals", "warnings", "quality_score", "status" ], "type": "object" } }, { "description": "Profils concurrents — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Answers: What are the strengths, weaknesses and positioning of each of my competitors? · Give me a SWOT-style profile of a named competitor. Reference case: Notion — profils de ClickUp, Asana, Coda. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "competitors": { "items": { "properties": { "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "url": { "format": "uri", "type": "string" } }, "required": [ "name", "url" ], "type": "object" }, "maxItems": 10, "minItems": 1, "type": "array" }, "focus": { "maxLength": 400, "type": "string" }, "selfCompany": { "properties": { "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "pitch": { "maxLength": 400, "minLength": 10, "type": "string" }, "url": { "format": "uri", "type": "string" } }, "required": [ "name", "pitch" ], "type": "object" } }, "required": [ "selfCompany", "competitors" ], "type": "object" }, "name": "competitor_profiles", "outputSchema": null }, { "description": "Recommandations concurrentielles — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Answers: Given my competitors, what strategic actions should I take and in what order? · What should my 7/30/90/180-day competitive response plan look like? Reference case: Notion — actions face à ClickUp, Asana, Coda. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "competitors": { "items": { "properties": { "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "url": { "format": "uri", "type": "string" } }, "required": [ "name", "url" ], "type": "object" }, "maxItems": 10, "minItems": 1, "type": "array" }, "focus": { "maxLength": 400, "type": "string" }, "selfCompany": { "properties": { "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "pitch": { "maxLength": 400, "minLength": 10, "type": "string" }, "url": { "format": "uri", "type": "string" } }, "required": [ "name", "pitch" ], "type": "object" } }, "required": [ "selfCompany", "competitors" ], "type": "object" }, "name": "competitor_recommendations", "outputSchema": null }, { "description": "Return the audience targeting profile of a content entity — its enrichment tags reframed as audience facets with confidence, corroboration and full provenance (verifiable, sourced). The response also carries an entity-level provenance block (average confidence, data freshness). When to use this tool: an ad-tech or marketing agent needs a machine-readable, verifiable audience descriptor for a franchise or work. Input: an entity_id and its type.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "entity_id": { "description": "Entity id from content_catalog", "type": "string" }, "entity_type": { "enum": [ "franchise", "work" ], "type": "string" } }, "required": [ "entity_id" ], "type": "object" }, "name": "content_audience_profile", "outputSchema": { "properties": { "audience_facets": { "additionalProperties": { "items": { "properties": { "confidence": { "type": "number" }, "corroboration": { "type": "number" }, "label": { "type": "string" }, "source_count": { "type": "integer" }, "sources": { "items": { "type": "object" }, "type": "array" } }, "type": "object" }, "type": "array" }, "description": "Map facet → array of { label, confidence, corroboration, source_count, sources }", "type": "object" }, "entity_id": { "type": "string" }, "entity_type": { "type": "string" }, "provenance": { "description": "Entity-level trust & freshness summary.", "properties": { "avg_confidence": { "type": "number" }, "avg_corroboration": { "type": "number" }, "freshness": { "enum": [ "fresh", "stale" ], "type": "string" }, "is_stale": { "type": "boolean" }, "last_refreshed": { "type": [ "string", "null" ] }, "oldest_age_days": { "type": [ "integer", "null" ] } }, "type": "object" } }, "required": [ "entity_id", "entity_type", "provenance", "audience_facets" ], "type": "object" } }, { "description": "Browse the Gapup gold-standard content catalogue — video games, films, TV series and music. Returns franchises with their works (title, release year). When to use this tool: an agent needs structured, audited metadata for a cultural franchise, wants to resolve a title to a canonical entity, or browses a domain's catalogue before requesting enrichment. Inputs: a content domain and an optional case-insensitive name filter. Each franchise id can be passed to content_enrichment for its fine-grained tag profile.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "domain": { "description": "Content domain to browse", "enum": [ "music", "film", "tv", "video-game" ], "type": "string" }, "limit": { "description": "Maximum number of franchises to return (default 20)", "maximum": 50, "minimum": 1, "type": "integer" }, "name": { "description": "Optional case-insensitive substring filter on franchise name", "maxLength": 120, "type": "string" } }, "required": [ "domain" ], "type": "object" }, "name": "content_catalog", "outputSchema": { "properties": { "count": { "type": "integer" }, "domain": { "type": "string" }, "franchises": { "items": { "properties": { "first_release_year": { "type": [ "integer", "null" ] }, "id": { "type": "string" }, "name": { "type": "string" }, "origin_country": { "type": [ "string", "null" ] }, "works": { "items": { "type": "object" }, "type": "array" } }, "type": "object" }, "type": "array" } }, "required": [ "domain", "count", "franchises" ], "type": "object" } }, { "description": "Compare the tag profiles of two content entities (franchises or works) and measure how similar they are. Returns a Jaccard similarity score, the list of shared tags, the tags unique to each entity, and a breakdown of shared tags by facet. When to use this tool: an agent needs to compare two franchises or works (e.g. 'how similar are Dark Souls and Elden Ring?', 'what do Street Fighter and Mortal Kombat have in common?', 'on which axes do these two games differ?'), find positioning overlap, identify cross-sell opportunities, or answer 'if you liked X you might like Y' questions backed by data. Works for any domain (video-games, music, film, tv).", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "entity_a": { "description": "Id of the first entity from content_catalog (e.g. 'game-dark-souls', 'music-daft-punk').", "type": "string" }, "entity_b": { "description": "Id of the second entity from content_catalog (e.g. 'game-elden-ring', 'music-justice').", "type": "string" }, "entity_type": { "description": "Whether both ids are franchises or works (applies to both). Defaults to 'franchise'.", "enum": [ "franchise", "work" ], "type": "string" } }, "required": [ "entity_a", "entity_b" ], "type": "object" }, "name": "content_compare", "outputSchema": { "properties": { "a_tag_count": { "type": "integer" }, "b_tag_count": { "type": "integer" }, "entity_a": { "type": "string" }, "entity_b": { "type": "string" }, "entity_type": { "type": "string" }, "shared_by_facet": { "additionalProperties": { "type": "integer" }, "description": "Count of shared tags per facet (e.g. { genre: 3, theme: 5 }). Shows which dimensions drive the similarity.", "type": "object" }, "shared_count": { "type": "integer" }, "shared_tags": { "description": "Tags present in both entities (up to 40).", "items": { "properties": { "facet": { "type": [ "string", "null" ] }, "label": { "type": "string" }, "tag_id": { "type": "string" } }, "type": "object" }, "type": "array" }, "similarity": { "description": "Jaccard index = |shared| / |union|, rounded to 2 decimal places. 0 = no overlap, 1 = identical profiles.", "type": "number" }, "unique_to_a": { "description": "Tags present only in entity_a (up to 40).", "items": { "properties": { "facet": { "type": [ "string", "null" ] }, "label": { "type": "string" }, "tag_id": { "type": "string" } }, "type": "object" }, "type": "array" }, "unique_to_b": { "description": "Tags present only in entity_b (up to 40).", "items": { "properties": { "facet": { "type": [ "string", "null" ] }, "label": { "type": "string" }, "tag_id": { "type": "string" } }, "type": "object" }, "type": "array" } }, "required": [ "entity_a", "entity_b", "entity_type", "similarity", "shared_count", "a_tag_count", "b_tag_count", "shared_tags", "unique_to_a", "unique_to_b", "shared_by_facet" ], "type": "object" } }, { "description": "Discover content franchises within a domain. Two modes: pass `tag` for a precise taxonomy match (every game tagged 'co-op'), or pass `query` for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "domain": { "description": "Content domain to search within", "enum": [ "music", "film", "tv", "video-game" ], "type": "string" }, "limit": { "description": "Maximum franchises to return (default 25)", "maximum": 50, "minimum": 1, "type": "integer" }, "query": { "description": "Free-text intent for semantic search (e.g. 'melancholic synth-pop about heartbreak'). Mutually exclusive with `tag`.", "maxLength": 400, "minLength": 3, "type": "string" }, "tag": { "description": "Tag label to match precisely (e.g. 'thriller', 'co-op'). Mutually exclusive with `query`.", "maxLength": 80, "minLength": 2, "type": "string" } }, "required": [ "domain" ], "type": "object" }, "name": "content_discovery", "outputSchema": { "properties": { "count": { "type": "integer" }, "domain": { "type": "string" }, "franchises": { "items": { "properties": { "entity_freshness": { "properties": { "avg_confidence": { "type": [ "number", "null" ] }, "freshness": { "enum": [ "fresh", "stale", "unknown" ], "type": "string" }, "is_stale": { "type": [ "boolean", "null" ] }, "oldest_age_days": { "type": [ "integer", "null" ] } }, "type": "object" }, "first_release_year": { "type": [ "integer", "null" ] }, "id": { "type": "string" }, "name": { "type": "string" }, "similarity": { "description": "Cosine similarity 0..1 (semantic method).", "type": [ "number", "null" ] }, "tag_provenance": { "description": "Provenance of the matching tag (tag method).", "properties": { "confidence": { "type": "number" }, "corroboration": { "type": "number" }, "source_count": { "type": "integer" } }, "type": [ "object", "null" ] } }, "type": "object" }, "type": "array" }, "method": { "enum": [ "tag", "semantic" ], "type": "string" }, "query": { "type": [ "string", "null" ] }, "tag": { "type": [ "string", "null" ] } }, "required": [ "domain", "method", "count", "franchises" ], "type": "object" } }, { "description": "Moteur de contenu — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Notion — content engine 2026 (productivity B2B). Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "brand": { "properties": { "audienceLabel": { "maxLength": 200, "minLength": 5, "type": "string" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "voiceAttributes": { "items": { "maxLength": 40, "minLength": 2, "type": "string" }, "maxItems": 5, "minItems": 2, "type": "array" } }, "required": [ "name", "voiceAttributes", "audienceLabel" ], "type": "object" }, "cluster": { "properties": { "geography": { "maxLength": 80, "type": "string" }, "primaryIntent": { "enum": [ "informational", "commercial", "transactional", "navigational" ], "type": "string" }, "topic": { "maxLength": 200, "minLength": 3, "type": "string" } }, "required": [ "topic", "primaryIntent" ], "type": "object" }, "maxArticlesPerMonth": { "maximum": 80, "minimum": 5, "type": "integer" }, "months": { "maximum": 12, "minimum": 3, "type": "integer" } }, "required": [ "brand", "cluster", "months", "maxArticlesPerMonth" ], "type": "object" }, "name": "content_engine", "outputSchema": null }, { "description": "Return the enriched tag profile of a content entity — the Gapup moat. Each tag carries a facet (genre, theme, play-mode, perspective…), a confidence score, a corroboration score and its full provenance (which sources corroborated it, when). The response also carries an entity-level provenance block (average confidence, data freshness). When to use this tool: an agent has a franchise or work id (from content_catalog) and needs a fine-grained, machine-readable, verifiable characterisation for matching, recommendation, contextual targeting or analysis. Inputs: an entity id and its type.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "entity_id": { "description": "Entity id from content_catalog (e.g. 'music-daft-punk', 'film-the-dark-knight-collection:the-dark-knight')", "type": "string" }, "entity_type": { "description": "Whether the id is a franchise or a work (default franchise)", "enum": [ "franchise", "work" ], "type": "string" } }, "required": [ "entity_id" ], "type": "object" }, "name": "content_enrichment", "outputSchema": { "properties": { "entity_id": { "type": "string" }, "entity_type": { "type": "string" }, "provenance": { "description": "Entity-level trust & freshness summary.", "properties": { "avg_confidence": { "type": "number" }, "avg_corroboration": { "type": "number" }, "freshness": { "enum": [ "fresh", "stale" ], "type": "string" }, "is_stale": { "type": "boolean" }, "last_refreshed": { "type": [ "string", "null" ] }, "oldest_age_days": { "type": [ "integer", "null" ] } }, "type": "object" }, "tag_count": { "type": "integer" }, "tags": { "items": { "properties": { "confidence": { "type": "number" }, "corroboration": { "type": "number" }, "extracted_by": { "type": [ "string", "null" ] }, "facet": { "type": [ "string", "null" ] }, "label": { "type": "string" }, "provenance": { "properties": { "fetched_at": { "type": [ "string", "null" ] }, "last_verified_at": { "type": [ "string", "null" ] }, "sources": { "items": { "type": "object" }, "type": "array" } }, "type": "object" }, "source_count": { "type": "integer" } }, "type": "object" }, "type": "array" } }, "required": [ "entity_id", "tag_count", "provenance", "tags" ], "type": "object" } }, { "description": "Evaluates content evergreen potential for CMOs by analyzing historical traffic patterns and backlink authority. Takes a content URL and optional time range, returns an evergreen score (0-100), traffic trend analysis, and backlink profile. Ideal for content strategy planning, SEO optimization, and identifying high-value evergreen assets. Uses Wayback Machine and Common Crawl public APIs.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "fromDate": { "description": "Start date for historical analysis (YYYY-MM-DD)", "format": "date", "type": "string" }, "toDate": { "description": "End date for historical analysis (YYYY-MM-DD)", "format": "date", "type": "string" }, "url": { "description": "Content URL to analyze", "format": "uri", "type": "string" } }, "required": [ "url" ], "type": "object" }, "name": "content_evergreen_score_analyzer", "outputSchema": { "properties": { "backlinkCount": { "minimum": 0, "type": "number" }, "backlinkDomains": { "minimum": 0, "type": "number" }, "evergreenScore": { "maximum": 100, "minimum": 0, "type": "number" }, "firstSeen": { "format": "date-time", "type": "string" }, "lastSeen": { "format": "date-time", "type": "string" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "trafficTrend": { "enum": [ "declining", "stable", "growing", "unknown" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status", "evergreenScore", "trafficTrend", "warnings", "sources" ], "type": "object" } }, { "description": "Audit the full data provenance of a content entity — all its enrichment tags with their extraction source, corroboration score, source list and last verification date, plus an entity-level freshness summary. Use this tool before citing or relying on enriched content data in a high-stakes context (ad targeting, editorial, analysis). Inputs: entity_id (required) and entity_type (franchise or work).", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "entity_id": { "description": "Entity id from content_catalog (e.g. 'video-game-elden-ring')", "type": "string" }, "entity_type": { "description": "Whether the id is a franchise or a work (default: franchise)", "enum": [ "franchise", "work" ], "type": "string" } }, "required": [ "entity_id" ], "type": "object" }, "name": "content_provenance", "outputSchema": { "properties": { "entity_id": { "type": "string" }, "entity_type": { "type": "string" }, "freshness_summary": { "description": "Entity-level freshness & trust summary from v_entity_freshness.", "properties": { "avg_confidence": { "type": [ "number", "null" ] }, "freshness": { "enum": [ "fresh", "stale", "unknown" ], "type": "string" }, "is_stale": { "type": [ "boolean", "null" ] }, "oldest_age_days": { "type": [ "integer", "null" ] }, "tag_count": { "type": [ "integer", "null" ] } }, "type": "object" }, "lineage": { "description": "Full tag lineage from v_data_lineage — one entry per tag.", "items": { "properties": { "confidence": { "type": "number" }, "corroboration": { "type": [ "number", "null" ] }, "domain": { "type": [ "string", "null" ] }, "extracted_by": { "type": [ "string", "null" ] }, "facet": { "type": [ "string", "null" ] }, "fetched_at": { "type": [ "string", "null" ] }, "last_verified_at": { "type": [ "string", "null" ] }, "source_count": { "type": [ "integer", "null" ] }, "sources": { "items": { "type": "object" }, "type": "array" }, "tag_id": { "type": "string" }, "tag_label": { "type": [ "string", "null" ] } }, "type": "object" }, "type": "array" } }, "required": [ "entity_id", "entity_type", "freshness_summary", "lineage" ], "type": "object" } }, { "description": "Return the TOP-ranked content entities in a category, by a chosen criterion — the direct answer to superlative / decision queries: 'best video games', 'top RPGs', 'cheapest games', 'best value RPGs', 'best FPS playable right now', 'most popular music artists'. Criteria: critic_score, popularity, price, value (critic score per unit price). `direction` flips it (asc = cheapest/lowest first). `available_only` restricts to entities currently buyable. Sliceable by genre and release-year window; every result carries its score, price and source. When to use: an agent must produce a ranked shortlist to support a recommendation, a purchase or a 'what is the best X' decision.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "available_only": { "description": "If true, restrict to entities currently available to buy/play (default false)", "type": "boolean" }, "criterion": { "description": "critic_score (0-100, default) · popularity · price · value (critic score per unit price)", "enum": [ "critic_score", "popularity", "price", "value" ], "type": "string" }, "direction": { "description": "desc = best/highest first (default); asc = cheapest/lowest/least first. Defaults to asc for price.", "enum": [ "desc", "asc" ], "type": "string" }, "domain": { "description": "Content domain to rank within", "enum": [ "music", "film", "tv", "video-game" ], "type": "string" }, "genre": { "description": "Optional genre filter, e.g. 'RPG', 'FPS', 'thriller'", "type": "string" }, "limit": { "description": "Number of ranked results (default 20)", "maximum": 50, "minimum": 1, "type": "integer" }, "year_from": { "description": "Optional earliest release year", "type": "integer" }, "year_to": { "description": "Optional latest release year", "type": "integer" } }, "required": [ "domain" ], "type": "object" }, "name": "content_ranking", "outputSchema": { "properties": { "available_only": { "type": "boolean" }, "count": { "type": "integer" }, "criterion": { "type": "string" }, "direction": { "type": "string" }, "domain": { "type": "string" }, "genre": { "type": [ "string", "null" ] }, "ranking": { "items": { "properties": { "available": { "type": [ "boolean", "null" ] }, "critic_score": { "type": [ "number", "null" ] }, "first_release_year": { "type": [ "integer", "null" ] }, "id": { "type": "string" }, "metric": { "description": "Value of the ranking criterion", "type": [ "number", "null" ] }, "name": { "type": "string" }, "price_cents": { "type": [ "integer", "null" ] }, "price_currency": { "type": [ "string", "null" ] }, "rank": { "type": "integer" }, "score_source": { "type": [ "string", "null" ] } }, "type": "object" }, "type": "array" }, "year_from": { "type": [ "integer", "null" ] }, "year_to": { "type": [ "integer", "null" ] } }, "required": [ "domain", "criterion", "count", "ranking" ], "type": "object" } }, { "description": "Find content entities similar to a given one. For embedded franchises this uses SEMANTIC vector similarity (pgvector) over the enrichment profile — surfacing entities that feel alike even when their tags differ literally. Falls back to shared enrichment-tag overlap for works or non-embedded entities. Each result carries a similarity score and its entity-level freshness/confidence (verifiable, sourced). When to use this tool: an agent wants recommendations or lookalikes for a franchise or work. Input: an entity_id and its type.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "entity_id": { "description": "Entity id from content_catalog", "type": "string" }, "entity_type": { "enum": [ "franchise", "work" ], "type": "string" }, "limit": { "maximum": 30, "minimum": 1, "type": "integer" } }, "required": [ "entity_id" ], "type": "object" }, "name": "content_similar", "outputSchema": { "properties": { "count": { "type": "integer" }, "entity_id": { "type": "string" }, "method": { "description": "How similarity was computed.", "enum": [ "semantic", "shared-tags" ], "type": "string" }, "similar": { "items": { "properties": { "entity_freshness": { "properties": { "avg_confidence": { "type": [ "number", "null" ] }, "freshness": { "enum": [ "fresh", "stale", "unknown" ], "type": "string" }, "is_stale": { "type": [ "boolean", "null" ] }, "oldest_age_days": { "type": [ "integer", "null" ] } }, "type": "object" }, "entity_id": { "type": "string" }, "name": { "type": [ "string", "null" ] }, "shared_tags": { "description": "Count of shared enrichment tags (shared-tags method).", "type": [ "integer", "null" ] }, "similarity": { "description": "Cosine similarity 0..1 (semantic method).", "type": [ "number", "null" ] } }, "type": "object" }, "type": "array" }, "source_provenance": { "description": "Provenance of the source entity used to compute similarity.", "properties": { "avg_confidence": { "type": [ "number", "null" ] }, "freshness": { "enum": [ "fresh", "stale", "unknown" ], "type": "string" }, "is_stale": { "type": [ "boolean", "null" ] }, "oldest_age_days": { "type": [ "integer", "null" ] } }, "type": "object" } }, "required": [ "entity_id", "method", "source_provenance", "count", "similar" ], "type": "object" } }, { "description": "Return the enrichment taxonomy of a content domain — every tag grouped by facet (genre, theme, mood, play-mode…). When to use this tool: an agent needs the controlled vocabulary to filter, classify or query content. Input: a domain.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "domain": { "description": "Content domain", "enum": [ "music", "film", "tv", "gaming" ], "type": "string" } }, "required": [ "domain" ], "type": "object" }, "name": "content_taxonomy", "outputSchema": { "properties": { "domain": { "type": "string" }, "facet_count": { "type": "integer" }, "tag_count": { "type": "integer" }, "taxonomy": { "additionalProperties": { "items": { "type": "string" }, "type": "array" }, "description": "Map facet → array of tag labels", "type": "object" } }, "required": [ "domain", "facet_count", "tag_count", "taxonomy" ], "type": "object" } }, { "description": "Scanner de risques contractuels — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Reference case: Salesforce MSA — revue d'un client SaaS B2B EMEA. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "contractContext": { "properties": { "contractType": { "enum": [ "MSA", "DPA", "NDA", "EULA", "vendor", "other" ], "type": "string" }, "counterparty": { "properties": { "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "dealSize": { "maxLength": 200, "type": "string" }, "ourRole": { "enum": [ "buyer", "seller" ], "type": "string" } }, "required": [ "ourRole", "contractType", "counterparty" ], "type": "object" }, "contractText": { "maxLength": 100000, "minLength": 500, "type": "string" }, "focus": { "maxLength": 500, "type": "string" } }, "required": [ "contractContext", "contractText" ], "type": "object" }, "name": "contract_risk_scanner", "outputSchema": null }, { "description": "Resolve legal information about a company from its national corporate registry. Returns a normalised, sourced company profile: legal status, registration number, directors, shareholders, recent filings, registered address, share capital, and a quality score (0–100). Coverage: France (INPI, keyless — full SIREN/SIRET with directors), 3M+ entities worldwide via GLEIF LEI (keyless, large companies), UK (Companies House, optional key), Netherlands (KvK, optional key), and OpenCorporates (token required since 2026). Sources are tried in cascade; quality_score increases with each source that succeeds. When to use: due-diligence, KYC screening, supplier verification, M&A research, or any workflow needing verified company identity and legal status. Optional env vars: COMPANIES_HOUSE_API_KEY (UK), KVK_API_KEY (NL), OPENCORPORATES_API_TOKEN (OpenCorporates token).", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company_name": { "description": "Company name or trading name to look up (e.g. 'Sanofi', 'Tesco PLC', 'Notion Labs Inc')", "maxLength": 200, "minLength": 2, "type": "string" }, "country": { "description": "ISO 3166-1 alpha-2 country code (e.g. 'FR', 'GB', 'NL', 'DE', 'SG', 'AU', 'US'). If omitted, inferred from legal suffix in company name, then falls back to global search.", "maxLength": 2, "minLength": 2, "type": "string" }, "identifier": { "description": "Optional registry identifier for a fast direct lookup: SIREN (FR, 9 digits), Companies House number (GB, 8 chars), KvK number (NL, 8 digits), etc.", "maxLength": 50, "type": "string" } }, "required": [ "company_name" ], "type": "object" }, "name": "corporate_registry_lookup", "outputSchema": { "properties": { "company_name": { "type": "string" }, "directors": { "items": { "properties": { "appointed": { "type": [ "string", "null" ] }, "name": { "type": "string" }, "role": { "type": "string" } }, "required": [ "name", "role" ], "type": "object" }, "type": "array" }, "filings_recent": { "items": { "properties": { "date": { "type": "string" }, "type": { "type": "string" }, "url": { "type": [ "string", "null" ] } }, "required": [ "date", "type" ], "type": "object" }, "type": "array" }, "freshness": { "description": "ISO timestamp", "type": "string" }, "identifier": { "type": "string" }, "incorporation_date": { "type": [ "string", "null" ] }, "jurisdiction": { "type": "string" }, "legal_form": { "type": [ "string", "null" ] }, "legal_name": { "type": [ "string", "null" ] }, "quality_score": { "description": "0-100 confidence score", "type": "number" }, "registered_address": { "type": [ "string", "null" ] }, "registry": { "type": "string" }, "share_capital": { "properties": { "amount": { "type": "number" }, "currency": { "type": "string" } }, "type": [ "object", "null" ] }, "shareholders": { "items": { "properties": { "name": { "type": "string" }, "percentage": { "type": [ "number", "null" ] } }, "required": [ "name" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "unavailable" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "active", "dissolved", "in_liquidation", "unknown" ], "type": "string" } }, "required": [ "company_name", "status", "jurisdiction", "registry", "identifier", "directors", "shareholders", "filings_recent", "sources", "quality_score", "freshness" ], "type": "object" } }, { "description": "Aggregate court filings, judgments and litigation records for a company or individual across five major legal jurisdictions: US (CourtListener / PACER), UK (National Archives — EWHC/EWCA/UKSC/UKUT), EU (ECHR HUDOC — European Court of Human Rights), France (Légifrance / Cour de cassation) and Germany (BGH / BVerfG). Returns structured case records with type classification (civil/criminal/antitrust/bankruptcy/administrative/unknown), status (filed/pending/decided/appealed/unknown), parties extracted from case titles, opinion URLs and verbatim snippets. Cross-case pattern recognition produces severity-ranked signals (P0–P2) for criminal, antitrust, bankruptcy, regulatory, data-breach and IP categories. Use when: due diligence on a counterparty, vendor risk assessment, competitive intelligence (litigation history), regulatory exposure mapping. All sources are public and keyless. Optional env var COURTLISTENER_API_KEY raises US rate limits beyond the default 5 req/s anonymous tier. SLA: ≤25s p95 (all jurisdictions fetched in parallel, 8s budget per source). Quality score: 20 pts per jurisdiction with ≥1 case retrieved, +10 if signals detected, +5–10 if ≥2–3 distinct sources contributed.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "date_from": { "description": "ISO date YYYY-MM-DD — earliest filing or decision date to include", "type": "string" }, "date_to": { "description": "ISO date YYYY-MM-DD — latest filing or decision date to include", "type": "string" }, "jurisdiction": { "description": "Jurisdictions to search. Defaults to all [\"US\",\"UK\",\"EU\",\"FR\",\"DE\"].", "items": { "enum": [ "US", "UK", "EU", "FR", "DE" ], "type": "string" }, "type": "array" }, "party_name": { "description": "Name of the company or individual to search (e.g. \"Apple Inc\", \"TotalEnergies\", \"Volkswagen AG\")", "maxLength": 200, "minLength": 2, "type": "string" } }, "required": [ "party_name" ], "type": "object" }, "name": "court_filings_multi", "outputSchema": { "properties": { "by_jurisdiction": { "additionalProperties": { "properties": { "count": { "type": "number" }, "latest_date": { "type": "string" } }, "required": [ "count" ], "type": "object" }, "type": "object" }, "cases": { "items": { "properties": { "case_id": { "type": "string" }, "court": { "type": "string" }, "date": { "type": "string" }, "jurisdiction": { "type": "string" }, "opinion_url": { "type": "string" }, "parties": { "items": { "type": "string" }, "type": "array" }, "snippet": { "type": "string" }, "source": { "type": "string" }, "status": { "enum": [ "filed", "pending", "decided", "appealed", "unknown" ], "type": "string" }, "title": { "type": "string" }, "type": { "enum": [ "civil", "criminal", "administrative", "bankruptcy", "antitrust", "unknown" ], "type": "string" } }, "required": [ "jurisdiction", "court", "case_id", "title", "date", "type", "status", "parties", "source" ], "type": "object" }, "type": "array" }, "jurisdictions_searched": { "items": { "enum": [ "US", "UK", "EU", "FR", "DE" ], "type": "string" }, "type": "array" }, "party_name": { "type": "string" }, "quality_score": { "type": "number" }, "signals": { "items": { "properties": { "category": { "enum": [ "criminal", "antitrust", "bankruptcy", "regulatory", "data_breach", "ip_dispute", "other" ], "type": "string" }, "evidence_case_ids": { "items": { "type": "string" }, "type": "array" }, "severity": { "enum": [ "P0", "P1", "P2" ], "type": "string" }, "summary": { "type": "string" } }, "required": [ "severity", "category", "summary", "evidence_case_ids" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "unavailable", "partial" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" } }, "required": [ "party_name", "jurisdictions_searched", "cases", "by_jurisdiction", "signals", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Push, update, search and log activities in HubSpot, Salesforce or Pipedrive. 4 modes: push_lead (create contact/lead), update_opportunity (update deal stage/amount), search_contact (lookup by email), log_activity (call/email/meeting/note). Returns resource_id, direct CRM URL, signals and quality_score. If credentials are absent, returns a mock result with a warning signal. Auth: HubSpot via Bearer access_token; Salesforce via access_token + base_url; Pipedrive via api_key.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "credentials": { "description": "Auth credentials. HubSpot: access_token. Salesforce: access_token + base_url. Pipedrive: api_key.", "properties": { "access_token": { "type": "string" }, "api_key": { "type": "string" }, "base_url": { "description": "Salesforce instance URL e.g. https://myorg.my.salesforce.com", "type": "string" } }, "type": "object" }, "data": { "additionalProperties": true, "description": "Payload depending on mode. push_lead: {email,first_name,last_name,company,phone,job_title}. update_opportunity: {deal_id/opportunity_id,stage,amount,close_date}. search_contact: {email}. log_activity: {type,body,contact_id/person_id,subject}.", "type": "object" }, "mode": { "description": "Action to perform in the CRM", "enum": [ "push_lead", "update_opportunity", "search_contact", "log_activity" ], "type": "string" }, "provider": { "description": "CRM provider to target", "enum": [ "salesforce", "hubspot", "pipedrive" ], "type": "string" } }, "required": [ "mode", "provider", "data" ], "type": "object" }, "name": "crm_connector", "outputSchema": { "properties": { "data_synced": {}, "mode": { "type": "string" }, "provider": { "type": "string" }, "quality_score": { "type": "number" }, "resource_id": { "type": "string" }, "signals": { "type": "array" }, "sources": { "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "success": { "type": "boolean" }, "url": { "type": "string" } }, "required": [ "mode", "provider", "success", "signals", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Recommandations cross-sell — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Alan × Gapup Hub — 3 produits recommandés · Fit 'perfect' × 2 · ARR potentiel +€18k. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "account": { "properties": { "arrEur": { "minimum": 0, "type": "number" }, "currentProducts": { "items": { "maxLength": 100, "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "size": { "enum": [ "smb", "mid-market", "enterprise", "strategic" ], "type": "string" }, "usageSignals": { "items": { "maxLength": 200, "type": "string" }, "maxItems": 5, "minItems": 0, "type": "array" } }, "required": [ "name", "arrEur", "currentProducts" ], "type": "object" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "portfolio": { "items": { "properties": { "category": { "maxLength": 100, "minLength": 2, "type": "string" }, "name": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "category" ], "type": "object" }, "maxItems": 10, "minItems": 2, "type": "array" } }, "required": [ "company", "account", "portfolio" ], "type": "object" }, "name": "cross_sell_reco", "outputSchema": null }, { "description": "Multi-chain on-chain analytics for crypto trading agents, on-chain analysts, AML/compliance teams and DeFi BD. Covers Ethereum, Base, Polygon, BSC, Arbitrum, Optimism — EVM-compatible addresses only.\n\n5 modes:\n• wallet_profile — full wallet summary: type (EOA/contract/CEX/protocol), inferred persona (whale/MEV-bot/DeFi-user/hodler…), age, tx count, native balance, ERC-20 count, NFT collections, OFAC sanctions flag\n• token_flows — ERC-20 inflows/outflows per token on the selected period, priced in USD via CoinGecko\n• pnl_estimate — FIFO realized + unrealized P&L on the period with confidence rating (high/medium/low)\n• counterparties — top 20 counterparties ranked by USD volume with CEX/DEX/protocol labels\n• defi_positions — active DeFi positions detected via Etherscan interaction history (Aave/Compound/Uniswap/Curve/Lido/Balancer/SushiSwap)\n\nSignal detection (P0/P1/P2):\n P0 if OFAC SDN match OR direct Tornado Cash / sanctioned-protocol interaction\n P1 if >$1M volume on wallet <30 days old OR MEV-bot pattern OR >80% volume on single counterparty\n P2 informational (CEX wallet, new wallet, no anomaly)\n\nSources: Etherscan family (keyless free-tier, optional API key per chain), DefiLlama (keyless), public EVM RPC (keyless), CoinGecko free tier (keyless).\nCache TTL: 5 min (wallet activity evolves fast). Budget: 8s per source.\n\nEnv vars (all optional, raise Etherscan rate-limit from 1 req/5s to 5 req/s):\n ETHERSCAN_API_KEY · BASESCAN_API_KEY · POLYGONSCAN_API_KEY\n BSCSCAN_API_KEY · ARBISCAN_API_KEY · OPTIMISM_API_KEY", "inputSchema": { "properties": { "address": { "description": "EVM-compatible wallet address (0x... 40 hex chars). Works on all supported chains.", "pattern": "^0x[0-9a-fA-F]{40}$", "type": "string" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "chain": { "description": "Chain to analyze. Default \"ethereum\". Use \"all\" to scan all 6 chains (slower, ~30s).", "enum": [ "ethereum", "base", "polygon", "bsc", "arbitrum", "optimism", "all" ], "type": "string" }, "min_value_usd": { "description": "Minimum USD value filter for token_flows and counterparties. Default $100.", "minimum": 0, "type": "number" }, "mode": { "description": "Analysis mode. wallet_profile=full wallet summary + persona + sanctions flag. token_flows=ERC-20 inflows/outflows per token priced in USD. pnl_estimate=FIFO realized+unrealized P&L with confidence. counterparties=top 20 counterparties by volume. defi_positions=active positions on Aave/Compound/Uniswap/Curve/Lido/etc.", "enum": [ "wallet_profile", "token_flows", "pnl_estimate", "counterparties", "defi_positions" ], "type": "string" }, "period_days": { "description": "Lookback window in days for token_flows, pnl_estimate, counterparties, defi_positions. Default 30.", "maximum": 365, "minimum": 1, "type": "number" } }, "required": [ "mode", "address" ], "type": "object" }, "name": "crypto_wallet_intel", "outputSchema": { "properties": { "address": { "type": "string" }, "chains_analyzed": { "items": { "type": "string" }, "type": "array" }, "counterparties": { "items": { "properties": { "address": { "type": "string" }, "label": { "type": "string" }, "side": { "type": "string" }, "tx_count": { "type": "number" }, "volume_usd": { "type": "number" } }, "type": "object" }, "type": "array" }, "defi_positions": { "items": { "properties": { "apy_pct": { "type": "number" }, "asset": { "type": "string" }, "chain": { "type": "string" }, "protocol": { "type": "string" }, "type": { "type": "string" }, "value_usd": { "type": "number" } }, "type": "object" }, "type": "array" }, "mode": { "type": "string" }, "pnl_estimate": { "properties": { "confidence": { "type": "string" }, "cost_basis_usd": { "type": "number" }, "period_days": { "type": "number" }, "realized_pnl_usd": { "type": "number" }, "unrealized_pnl_usd": { "type": "number" } }, "type": "object" }, "quality_score": { "type": "number" }, "signals": { "items": { "properties": { "category": { "type": "string" }, "message": { "type": "string" }, "severity": { "type": "string" } }, "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "status": { "type": "string" }, "token_flows": { "items": { "properties": { "inflows_usd": { "type": "number" }, "last_tx_iso": { "type": "string" }, "net_usd": { "type": "number" }, "outflows_usd": { "type": "number" }, "token_address": { "type": "string" }, "token_symbol": { "type": "string" }, "tx_count": { "type": "number" } }, "type": "object" }, "type": "array" }, "wallet_profile": { "properties": { "address": { "type": "string" }, "age_days": { "type": "number" }, "chains_active": { "items": { "type": "string" }, "type": "array" }, "erc20_count": { "type": "number" }, "first_tx": { "type": "string" }, "inferred_persona": { "type": "string" }, "inferred_type": { "type": "string" }, "native_balance_eth": { "type": "number" }, "native_balance_usd": { "type": "number" }, "nft_collections_count": { "type": "number" }, "sanctions_flag": { "type": "boolean" }, "total_tx_count": { "type": "number" } }, "type": "object" } }, "required": [ "mode", "address", "chains_analyzed", "signals", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Marketing clients & ambassadeurs — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Gapup Hub — 12 clients analysés · 4 ambassadeurs identifiés · Programme + 6 case studies + référral. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 100, "type": "string" }, "sector": { "maxLength": 100, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "contentBudgetEur": { "minimum": 0, "type": "number" }, "customers": { "items": { "properties": { "arrEur": { "minimum": 0, "type": "number" }, "id": { "maxLength": 20, "type": "string" }, "industry": { "maxLength": 100, "type": "string" }, "keyContact": { "maxLength": 100, "type": "string" }, "name": { "maxLength": 100, "type": "string" }, "npsScore": { "maximum": 10, "minimum": 0, "type": "number" }, "referenceWilling": { "type": "boolean" }, "tenure": { "maxLength": 50, "type": "string" }, "usageRatePct": { "maximum": 100, "minimum": 0, "type": "number" }, "useCase": { "maxLength": 300, "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 30, "minItems": 3, "type": "array" }, "goals": { "items": { "enum": [ "case-studies", "testimonials", "referrals", "ambassadors", "community", "upsell" ], "type": "string" }, "maxItems": 6, "minItems": 1, "type": "array" }, "product": { "properties": { "name": { "maxLength": 100, "type": "string" } }, "required": [ "name" ], "type": "object" }, "targetUseCases": { "items": { "maxLength": 200, "type": "string" }, "maxItems": 5, "type": "array" } }, "required": [ "company", "product", "customers", "goals" ], "type": "object" }, "name": "customer_marketing", "outputSchema": null }, { "description": "Synthèse voix client — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Alan (assurance santé) — 3 personas · Top 5 douleurs · Repositionnement messagerie recommandé. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "product": { "maxLength": 100, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "product" ], "type": "object" }, "dataSources": { "items": { "properties": { "description": { "maxLength": 200, "type": "string" }, "type": { "enum": [ "reviews", "support-tickets", "nps-verbatims", "user-interviews", "social", "churn-surveys" ], "type": "string" }, "volume": { "minimum": 0, "type": "number" } }, "required": [ "type", "volume" ], "type": "object" }, "maxItems": 6, "minItems": 1, "type": "array" }, "repositioningFocus": { "maxLength": 300, "minLength": 10, "type": "string" }, "targetSegments": { "items": { "maxLength": 80, "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" } }, "required": [ "company", "dataSources", "targetSegments" ], "type": "object" }, "name": "customer_voice_synth", "outputSchema": null }, { "description": "Look up CVE vulnerability data for enterprise security teams, DevSecOps and SOC analysts. Supports two modes: exact CVE ID lookup (e.g. 'CVE-2024-3094') or keyword search by product/vendor (e.g. 'openssl', 'Apache Tomcat'). Cross-references four authoritative keyless sources: NVD NIST (official CVE database, CVSS v3 scores, affected CPEs), CISA KEV (Known Exploited Vulnerabilities catalog — exploit_in_wild flag), EPSS FIRST (exploit probability 0-1), GitHub Security Advisories (ecosystem-specific: npm/pypi/maven). Returns structured vulnerability records with CVSS v3 scores, affected product version ranges, CWE weakness classification, references and exploitation status. Signals engine produces P0/P1/P2 alerts: P0=CVSS>=9 + active exploitation, P1=CVSS>=7 or EPSS>=70%, P2=CWE pattern clusters. Relevant for EU NIS2 and DORA supply chain risk obligations. Optional env: NVD_API_KEY (raises NVD rate-limit 5→50 req/30s), GITHUB_TOKEN (raises GHSA GraphQL rate-limit). Cache TTL 6h. SLA <=25s p95.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "max_results": { "description": "Maximum number of vulnerabilities to return (default 20, max 50).", "maximum": 50, "minimum": 5, "type": "number" }, "mode": { "description": "Override auto-detection: \"lookup\" for exact CVE ID, \"search\" for product/vendor keyword.", "enum": [ "lookup", "search" ], "type": "string" }, "published_after": { "description": "ISO date YYYY-MM-DD — only include CVEs published after this date. Defaults to 365 days ago for search mode.", "type": "string" }, "query": { "description": "CVE ID (e.g. \"CVE-2024-3094\") or product/vendor keyword (e.g. \"openssl\", \"Apache Tomcat\"). Mode is auto-detected from the CVE-YYYY-XXXXX pattern.", "maxLength": 200, "minLength": 3, "type": "string" }, "severity_min": { "description": "Minimum CVSS v3 severity to include in results (default: no filter).", "enum": [ "low", "medium", "high", "critical" ], "type": "string" } }, "required": [ "query" ], "type": "object" }, "name": "cve_security_lookup", "outputSchema": { "properties": { "mode": { "enum": [ "lookup", "search" ], "type": "string" }, "quality_score": { "type": "number" }, "query": { "type": "string" }, "signals": { "items": { "properties": { "evidence_cve_ids": { "items": { "type": "string" }, "type": "array" }, "severity": { "enum": [ "P0", "P1", "P2" ], "type": "string" }, "summary": { "type": "string" } }, "required": [ "severity", "summary", "evidence_cve_ids" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "unavailable", "partial" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "vulnerabilities": { "items": { "properties": { "affected_products": { "items": { "properties": { "product": { "type": "string" }, "vendor": { "type": "string" }, "version_ranges": { "items": { "type": "string" }, "type": "array" } }, "required": [ "vendor", "product", "version_ranges" ], "type": "object" }, "type": "array" }, "cve_id": { "type": "string" }, "cvss_v3_score": { "type": "number" }, "cvss_v3_severity": { "enum": [ "low", "medium", "high", "critical" ], "type": "string" }, "cvss_v3_vector": { "type": "string" }, "cwe_ids": { "items": { "type": "string" }, "type": "array" }, "description": { "type": "string" }, "epss_percentile": { "type": "number" }, "epss_score": { "type": "number" }, "exploit_available": { "type": "boolean" }, "exploit_in_wild": { "type": "boolean" }, "last_modified": { "type": "string" }, "patch_available": { "type": "boolean" }, "published_date": { "type": "string" }, "references": { "items": { "properties": { "source": { "type": "string" }, "tags": { "items": { "type": "string" }, "type": "array" }, "url": { "type": "string" } }, "required": [ "url", "source" ], "type": "object" }, "type": "array" }, "threat_actors": { "items": { "type": "string" }, "type": "array" }, "title": { "type": "string" } }, "required": [ "cve_id", "description", "affected_products", "cwe_ids", "references", "published_date", "last_modified" ], "type": "object" }, "type": "array" } }, "required": [ "query", "mode", "vulnerabilities", "signals", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Auditeur de risque cyber — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Reference case: Qonto — Audit cyber risque B2B FinTech · Score 58/100 → roadmap 90j · 8 findings critiques/high · économie prime -28%. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "dataTypes": { "items": { "type": "string" }, "maxItems": 6, "minItems": 1, "type": "array" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" }, "size": { "enum": [ "startup", "sme", "midmarket", "enterprise" ], "type": "string" } }, "required": [ "name", "sector", "size", "dataTypes" ], "type": "object" }, "currentPosture": { "properties": { "hasPenTest": { "type": "boolean" }, "hasSOC": { "type": "boolean" }, "hasSOC2orISO27001": { "type": "boolean" }, "incidentPlanExists": { "type": "boolean" }, "mfa": { "type": "boolean" } }, "required": [ "hasSOC", "hasPenTest", "hasSOC2orISO27001", "mfa", "incidentPlanExists" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" }, "techStack": { "properties": { "cloudProviders": { "items": { "type": "string" }, "maxItems": 5, "minItems": 0, "type": "array" }, "endpointMgmt": { "maxLength": 100, "type": "string" }, "identityProvider": { "maxLength": 100, "type": "string" }, "keyApps": { "items": { "type": "string" }, "maxItems": 8, "minItems": 0, "type": "array" } }, "required": [ "cloudProviders", "keyApps" ], "type": "object" } }, "required": [ "company", "techStack", "currentPosture" ], "type": "object" }, "name": "cyber_risk_auditor", "outputSchema": null }, { "description": "Coach de deal MEDDIC — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Datadog Enterprise deal Société Générale €1.2M ARR — coaching MEDDIC + escalation plays + 14 next actions. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "buyingCommittee": { "items": { "properties": { "name": { "type": "string" }, "role": { "enum": [ "champion", "economic-buyer", "user", "influencer", "blocker" ], "type": "string" }, "title": { "type": "string" } }, "required": [ "name", "title", "role" ], "type": "object" }, "maxItems": 8, "minItems": 2, "type": "array" }, "deal": { "properties": { "accountName": { "type": "string" }, "amountEur": { "type": "number" }, "daysInStage": { "type": "integer" }, "expectedCloseDate": { "type": "string" }, "ownerRep": { "type": "string" }, "stage": { "type": "string" } }, "required": [ "accountName", "amountEur", "stage", "daysInStage", "expectedCloseDate", "ownerRep" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" }, "knownContext": { "maxLength": 800, "minLength": 20, "type": "string" } }, "required": [ "deal", "buyingCommittee", "knownContext" ], "type": "object" }, "name": "deal_coach", "outputSchema": null }, { "description": "Structuration de deal — Gapup agent-payable C-suite expertise (CSO). Returns a structured, audited deliverable. Reference case: Agicap × Kyriba — Partenariat API Banking · 5 structures comparées · Term sheet 7 clauses · Score 83/100 JV. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "deal": { "properties": { "constraints": { "items": { "maxLength": 200, "type": "string" }, "maxItems": 5, "minItems": 0, "type": "array" }, "estimatedValue": { "maxLength": 100, "minLength": 2, "type": "string" }, "objective": { "maxLength": 300, "minLength": 10, "type": "string" }, "partnerName": { "maxLength": 200, "minLength": 2, "type": "string" }, "partnerProfile": { "maxLength": 500, "minLength": 10, "type": "string" } }, "required": [ "partnerName", "partnerProfile", "objective" ], "type": "object" } }, "required": [ "company", "deal" ], "type": "object" }, "name": "deal_structurer", "outputSchema": null }, { "description": "SCA (Software Composition Analysis) — scans a project dependency manifest and returns known vulnerabilities for each dependency. Supports: package.json (npm), requirements.txt (Python), go.mod (Go), Cargo.toml (Rust), composer.json (PHP), Gemfile.lock (Ruby), CycloneDX SBOM JSON. PRIMARY source: OSV.dev (keyless, free, covers npm/PyPI/Go/crates.io/Packagist/RubyGems + GHSA advisories federated). CVSS enrichment: NVD NIST (when OSV lacks score). Exploitation flag: CISA KEV (known-exploited-vulnerabilities catalog). Returns per-vuln CVE/GHSA IDs, severity, CVSS score, fixed version, and actionable upgrade recommendations. Relevant for EU NIS2 supply chain risk obligations, DORA, SOC 2 vendor assessments. Cache TTL 6h. Parallel OSV queries (concurrency=10). SLA <=30s p95.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "include_transitive": { "description": "Include transitive/indirect dependencies in results (default: true).", "type": "boolean" }, "manifest_content": { "description": "Raw text content of the manifest file to scan (e.g. full contents of package.json, requirements.txt, etc.).", "type": "string" }, "mode": { "description": "Manifest type: \"package_json\"=npm, \"requirements_txt\"=pip, \"go_mod\"=Go modules, \"cargo_toml\"=Rust, \"composer_json\"=PHP, \"gem_lock\"=Ruby, \"sbom_cyclonedx\"=CycloneDX SBOM JSON.", "enum": [ "package_json", "requirements_txt", "go_mod", "cargo_toml", "composer_json", "gem_lock", "sbom_cyclonedx" ], "type": "string" }, "severity_min": { "description": "Minimum severity to include in results (default: \"medium\").", "enum": [ "low", "medium", "high", "critical" ], "type": "string" } }, "required": [ "mode", "manifest_content" ], "type": "object" }, "name": "dependency_vulnerability_scan", "outputSchema": { "properties": { "dependencies_parsed": { "type": "number" }, "ecosystem": { "enum": [ "npm", "pypi", "go", "crates", "packagist", "rubygems", "sbom" ], "type": "string" }, "mode": { "type": "string" }, "quality_score": { "type": "number" }, "recommendations": { "items": { "properties": { "action": { "type": "string" }, "justification": { "type": "string" }, "package": { "type": "string" } }, "required": [ "package", "action", "justification" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "unavailable", "partial" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "summary": { "properties": { "actionable_count": { "type": "number" }, "breaking_changes_pct": { "type": "number" }, "critical": { "type": "number" }, "high": { "type": "number" }, "low": { "type": "number" }, "medium": { "type": "number" }, "total_dependencies": { "type": "number" }, "vulnerable_count": { "type": "number" } }, "required": [ "total_dependencies", "vulnerable_count", "critical", "high", "medium", "low", "actionable_count", "breaking_changes_pct" ], "type": "object" }, "vulnerabilities": { "items": { "properties": { "advisory_summary": { "type": "string" }, "advisory_url": { "type": "string" }, "cve_ids": { "items": { "type": "string" }, "type": "array" }, "cvss_score": { "type": "number" }, "fixed_in_version": { "type": "string" }, "ghsa_ids": { "items": { "type": "string" }, "type": "array" }, "installed_version": { "type": "string" }, "package": { "type": "string" }, "severity": { "enum": [ "low", "medium", "high", "critical" ], "type": "string" }, "transitive": { "type": "boolean" }, "vulnerable_versions": { "type": "string" } }, "required": [ "package", "installed_version", "vulnerable_versions", "cve_ids", "ghsa_ids", "severity", "advisory_summary", "advisory_url", "transitive" ], "type": "object" }, "type": "array" } }, "required": [ "mode", "ecosystem", "dependencies_parsed", "vulnerabilities", "summary", "recommendations", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Préparation discovery — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Discovery Salesforce × Airbus — VP Digital Marc Legrand · Signaux achat confirmés · +28 pts conversion demo. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "contact": { "properties": { "name": { "maxLength": 100, "minLength": 2, "type": "string" }, "title": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "name", "title" ], "type": "object" }, "meetingGoal": { "maxLength": 300, "type": "string" }, "ourOffer": { "maxLength": 400, "minLength": 10, "type": "string" }, "prospect": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" }, "size": { "enum": [ "startup", "sme", "midmarket", "enterprise" ], "type": "string" }, "url": { "format": "uri", "type": "string" } }, "required": [ "name", "sector" ], "type": "object" } }, "required": [ "prospect", "contact", "ourOffer" ], "type": "object" }, "name": "discovery_prep", "outputSchema": null }, { "description": "Métriques diversité & inclusion — Gapup agent-payable C-suite expertise (SUSTAINABILITY). Returns a structured, audited deliverable. Reference case: Cas démo — Métriques diversité & inclusion. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "ambitions": { "maxLength": 500, "minLength": 15, "type": "string" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "fte": { "minimum": 0, "type": "integer" }, "geographies": { "items": { "maxLength": 60, "minLength": 2, "type": "string" }, "maxItems": 12, "minItems": 1, "type": "array" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "sector": { "maxLength": 80, "minLength": 2, "type": "string" }, "stage": { "maxLength": 40, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "fte", "stage" ], "type": "object" }, "currentState": { "properties": { "ergCount": { "minimum": 0, "type": "integer" }, "genderRepresentationPct": { "properties": { "exec": { "maximum": 100, "minimum": 0, "type": "number" }, "management": { "maximum": 100, "minimum": 0, "type": "number" }, "overall": { "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "overall", "management", "exec" ], "type": "object" }, "knownChallenges": { "items": { "maxLength": 200, "minLength": 5, "type": "string" }, "maxItems": 8, "minItems": 1, "type": "array" }, "nationalityCount": { "minimum": 0, "type": "integer" }, "payEquityGapPct": { "type": "number" } }, "required": [ "genderRepresentationPct", "knownChallenges" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" }, "regulatoryContext": { "items": { "type": "string" }, "maxItems": 8, "type": "array" } }, "required": [ "company", "currentState", "ambitions" ], "type": "object" }, "name": "diversity_inclusion_metrics", "outputSchema": null }, { "description": "Empreinte tech d'un domaine — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Answers: What is the tech stack of <domain> — frontend, CMS, analytics, CRM, CDN, hosting? · What buying signals does <domain>'s technology footprint reveal for sales prospecting? · Analyze <domain> for supply-chain technology risk and third-party vendor exposure. · What is the best outreach angle for a sales rep targeting <domain> based on their detected stack? · Run a CISO-style technology fingerprint on <domain> — identify legacy tech, missing security headers, and vendor risk. · Has <domain> recently changed their marketing or analytics stack — any vendor adoption signals? Reference case: velora-payments.io · Next.js + Cloudflare + Stripe + GA4 + HubSpot · . Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "depth": { "default": "standard", "enum": [ "quick", "standard", "deep" ], "type": "string" }, "focus": { "default": "tech-buying", "enum": [ "tech-buying", "supply-chain", "general" ], "type": "string" }, "target_domain": { "maxLength": 253, "minLength": 4, "type": "string" } }, "required": [ "target_domain", "focus", "depth" ], "type": "object" }, "name": "domain_tech_fingerprint", "outputSchema": null }, { "description": "Analyzes DORA metrics (Deployment Frequency, Mean Time to Recovery, Change Failure Rate) with deep correlation to code review patterns. Designed for CTOs to identify bottlenecks in software delivery pipelines. Inputs include GitHub repository identifiers and optional time ranges. Outputs structured metrics with trend analysis and code review depth insights.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "branch": { "description": "Branch name to analyze (default: main)", "type": "string" }, "repo": { "description": "GitHub repository in format 'owner/repo'", "type": "string" }, "since": { "description": "Start date for analysis (ISO 8601)", "format": "date-time", "type": "string" }, "until": { "description": "End date for analysis (ISO 8601)", "format": "date-time", "type": "string" } }, "required": [ "repo" ], "type": "object" }, "name": "dora_metrics_deep_dive", "outputSchema": { "properties": { "metrics": { "properties": { "change_failure_rate": { "description": "Percentage of failed changes", "type": "number" }, "deploy_frequency": { "description": "Deployments per day", "type": "number" }, "mttr": { "description": "Mean Time to Recovery in hours", "type": "number" }, "review_depth": { "properties": { "approval_rate": { "description": "Percentage of PRs approved", "type": "number" }, "avg_comments": { "type": "number" }, "avg_review_time": { "description": "Average review time in hours", "type": "number" } }, "type": "object" }, "trends": { "properties": { "deploy_frequency_trend": { "description": "Trend slope for deploy frequency", "type": "number" }, "mttr_trend": { "description": "Trend slope for MTTR", "type": "number" } }, "type": "object" } }, "type": "object" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Assess DORA operational resilience by simulating ICT failure scenarios for financial entities. Designed for legal/compliance teams to evaluate ICT risk management under DORA Article 25. Inputs include failure scenario parameters (e.g., ICT service type, duration, impact radius) and entity profile. Outputs structured resilience scores, regulatory gaps, and mitigation recommendations with EUR-Lex/FTC enforcement references.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "entityType": { "enum": [ "credit-institution", "investment-firm", "payment-institution", "crypto-asset" ], "type": "string" }, "existingMitigations": { "items": { "enum": [ "redundancy", "backup", "failover", "contractual-sla", "none" ], "type": "string" }, "type": "array" }, "failureDurationHours": { "maximum": 72, "minimum": 0.5, "type": "number" }, "ictServiceType": { "enum": [ "cloud", "network", "payment", "core", "third-party" ], "type": "string" }, "impactRadius": { "enum": [ "single-entity", "multi-entity", "sector-wide" ], "type": "string" } }, "required": [ "ictServiceType", "failureDurationHours", "impactRadius", "entityType" ], "type": "object" }, "name": "dora_operational_resilience_stress_tes", "outputSchema": { "properties": { "recommendedMitigations": { "items": { "type": "string" }, "type": "array" }, "regulatoryGaps": { "items": { "properties": { "article": { "type": "string" }, "gapDescription": { "type": "string" }, "severity": { "enum": [ "low", "medium", "high" ], "type": "string" } }, "type": "object" }, "type": "array" }, "resilienceScore": { "maximum": 100, "minimum": 0, "type": "number" }, "simulationTimestamp": { "format": "date-time", "type": "string" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status", "resilienceScore", "regulatoryGaps", "recommendedMitigations", "sources" ], "type": "object" } }, { "description": "Generates structured consent artifacts compliant with India's Digital Personal Data Protection Act (DPDP). Designed for legal teams to verify or create consent records with timestamped logs, purpose limitation, and data subject rights. Accepts data subject details, processing purpose, and legal basis as inputs. Returns a signed artifact with audit trail and validation status.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "dataCategories": { "description": "Categories of personal data being processed", "items": { "type": "string" }, "type": "array" }, "dataSubjectId": { "description": "Unique identifier for the data subject", "type": "string" }, "legalBasis": { "description": "Legal basis for processing under DPDP", "enum": [ "consent", "contract", "legal_obligation", "vital_interests", "public_task", "legitimate_interests" ], "type": "string" }, "processingPurpose": { "description": "Specific purpose for data processing", "type": "string" }, "retentionPeriodDays": { "description": "Retention period in days", "type": "number" } }, "required": [ "dataSubjectId", "processingPurpose", "legalBasis" ], "type": "object" }, "name": "dpdp_consent_artifact_generator", "outputSchema": { "properties": { "artifact": { "properties": { "auditTrail": { "items": { "properties": { "actor": { "type": "string" }, "event": { "type": "string" }, "timestamp": { "format": "date-time", "type": "string" } }, "required": [ "event", "timestamp", "actor" ], "type": "object" }, "type": "array" }, "consentId": { "type": "string" }, "dataCategories": { "items": { "type": "string" }, "type": "array" }, "dataSubjectId": { "type": "string" }, "legalBasis": { "type": "string" }, "processingPurpose": { "type": "string" }, "retentionPeriodDays": { "type": "number" }, "signature": { "type": "string" }, "timestamp": { "format": "date-time", "type": "string" } }, "required": [ "consentId", "timestamp", "dataSubjectId", "processingPurpose", "legalBasis", "signature" ], "type": "object" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "As a COO, quickly assess export compliance risks for components in your supply chain. This tool analyzes bills of materials (BOMs) against EU dual-use export control lists and ICAO/IMO restricted items data. Input a list of part numbers, descriptions, or HS codes to receive a risk assessment with actionable insights. Output includes risk levels, applicable regulations, and source references for audit trails.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "bomItems": { "items": { "properties": { "description": { "type": "string" }, "hsCode": { "type": "string" }, "partNumber": { "type": "string" }, "quantity": { "type": "number" } }, "required": [ "description" ], "type": "object" }, "type": "array" }, "includeSources": { "default": false, "type": "boolean" } }, "required": [ "bomItems" ], "type": "object" }, "name": "dual_use_export_risk_mapper", "outputSchema": { "properties": { "results": { "items": { "properties": { "description": { "type": "string" }, "hsCode": { "type": "string" }, "mitigation": { "type": "string" }, "partNumber": { "type": "string" }, "regulations": { "items": { "properties": { "code": { "type": "string" }, "description": { "type": "string" }, "jurisdiction": { "type": "string" } }, "type": "object" }, "type": "array" }, "riskLevel": { "enum": [ "high", "medium", "low", "none" ], "type": "string" } }, "required": [ "description", "riskLevel" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "fetched_at": { "type": "string" }, "name": { "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Asynchronous T5-level tool for COO persona to detect unauthorized diversion of dual-use technologies. Cross-references shipment manifests, EU sanctions lists, and ICAO/IMO transport data to identify suspicious transfers. Inputs: shipment IDs, company identifiers, or geographic routes. Outputs structured diversion risk assessment with source provenance. Requires async:true to avoid 402 timeout.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "companyId": { "description": "Company registration number or tax identifier", "type": "string" }, "route": { "properties": { "destination": { "description": "ISO 3166-1 alpha-3 country code", "type": "string" }, "origin": { "description": "ISO 3166-1 alpha-3 country code", "type": "string" } }, "required": [ "origin", "destination" ], "type": "object" }, "shipmentId": { "description": "Unique shipment identifier (e.g., bill of lading number)", "type": "string" }, "techCategory": { "description": "Dual-use technology category", "enum": [ "aerospace", "nuclear", "biotech", "semiconductors", "telecom" ], "type": "string" } }, "required": [], "type": "object" }, "name": "dual_use_tech_diversion_monitor", "outputSchema": { "properties": { "diversionRisk": { "description": "Calculated diversion risk score (0-100)", "maximum": 100, "minimum": 0, "type": "number" }, "matches": { "items": { "properties": { "confidence": { "maximum": 1, "minimum": 0, "type": "number" }, "entity": { "type": "string" }, "matchType": { "enum": [ "sanctioned_entity", "unusual_route", "restricted_tech", "anomalous_shipment" ], "type": "string" }, "source": { "type": "string" } }, "required": [ "entity", "matchType", "confidence" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "dataset": { "enum": [ "eu_sanctions", "icao_flight", "imo_vessel", "customs_manifest" ], "type": "string" }, "fetched_at": { "format": "date-time", "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Earnings Reviewer — Gapup agent-payable C-suite expertise (FUNDRAISING). Returns a structured, audited deliverable. Reference case: Salesforce Q3 FY2026 — call transcript + 10-Q + guidance → analyst note. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "analystFocus": { "maxLength": 1000, "type": "string" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "exchange": { "default": "NASDAQ", "enum": [ "NYSE", "NASDAQ", "LSE", "Euronext", "TSX", "other" ], "type": "string" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" }, "ticker": { "maxLength": 10, "minLength": 1, "type": "string" } }, "required": [ "name", "ticker", "sector", "exchange" ], "type": "object" }, "quarter": { "properties": { "label": { "maxLength": 30, "minLength": 2, "type": "string" }, "reportDate": { "maxLength": 30, "type": "string" } }, "required": [ "label" ], "type": "object" }, "secFilingContext": { "maxLength": 20000, "type": "string" }, "transcriptExcerpt": { "maxLength": 50000, "minLength": 200, "type": "string" } }, "required": [ "company", "quarter", "transcriptExcerpt" ], "type": "object" }, "name": "earnings_reviewer", "outputSchema": null }, { "description": "Earnings call transcript signal extractor for equity research analysts, catalyst-driven hedge funds, and BD teams. Parses earnings transcripts (fetched or provided) to surface:\n\n• signals (P0/P1/P2): guidance raise/cut, miss/beat vs consensus, buyback, dividend change, new product, executive change, capex shift, M&A intent, regulatory risk, competitive threat, supply chain, hiring\n• kpis_mentioned: Revenue, EBITDA, EPS, FCF, Gross Margin, Operating Margin with YoY/QoQ %\n• guidance: raised / maintained / cut / new_initiated items extracted\n• q_and_a_topics: top Q&A themes detected (AI strategy, China exposure, M&A pipeline, macro, etc.)\n• overall_tone: bullish / neutral / bearish\n\nSources fetched automatically: SEC EDGAR 8-K filings, Yahoo Finance earnings news, Motley Fool transcripts. If no transcript can be retrieved from any source, returns status:'failed' with an explicit warning and empty signals — never fabricated data. Accepts transcript_text override for direct analysis. Supports multilingual transcripts (de/fr/es/zh). European tickers (SAP.DE, BMW.DE) mapped to EDGAR-compatible equivalents automatically.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company_or_ticker": { "description": "Company name or ticker symbol (e.g. 'Tesla', 'TSLA', 'SAP', 'SAP.DE', 'Sanofi', 'SNY'). European tickers (SAP.DE, BMW.DE) are mapped to their ADR equivalents for EDGAR lookup.", "type": "string" }, "lang": { "description": "Language hint for the transcript. Affects mock transcript language when fetch fails.", "enum": [ "en", "de", "fr", "es", "zh" ], "type": "string" }, "quarter": { "description": "Fiscal quarter in format Q1-2026. Defaults to the most recent past quarter.", "pattern": "^Q[1-4]-\\d{4}$", "type": "string" }, "transcript_text": { "description": "If provided, skips all external fetches and analyses this text directly. Minimum 100 characters.", "type": "string" } }, "required": [ "company_or_ticker" ], "type": "object" }, "name": "earnings_transcript_signals", "outputSchema": null }, { "description": "Return a precise macroeconomic indicator for a country — the exact figure for a market-sizing, finance or strategy workflow. Indicators: gdp_usd, gdp_per_capita, gdp_growth, inflation, unemployment, population. Source: World Bank. When to use: an agent's analysis needs an authoritative country-level economic figure. Inputs: country (ISO-2 or ISO-3 code) and indicator name.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "country": { "description": "Country code, ISO-2 or ISO-3 (e.g. FR, USA)", "type": "string" }, "indicator": { "description": "Macroeconomic indicator name", "enum": [ "gdp_usd", "gdp_per_capita", "gdp_growth", "inflation", "unemployment", "population" ], "type": "string" } }, "required": [ "country", "indicator" ], "type": "object" }, "name": "economic_indicator", "outputSchema": { "properties": { "country": { "type": "string" }, "indicator": { "type": "string" }, "indicator_code": { "type": "string" }, "source": { "type": "string" }, "source_url": { "type": "string" }, "value": { "type": [ "number", "null" ] }, "year": { "type": [ "string", "null" ] } }, "required": [ "country", "indicator", "value", "year", "source" ], "type": "object" } }, { "description": "Comprehensive email domain health check: MX routing, SPF authentication, DKIM signing, DMARC policy enforcement, DNSBL blacklist status (Spamhaus/SpamCop/Barracuda), TLS certificate validity, and WHOIS registration age. Aggregates a reputation score 0-100 and generates P0/P1/P2 deliverability signals. Accepts a domain (stripe.com) or email address ([email protected]). Detects role-based addresses (info@, support@, admin@, noreply@) that have higher bounce rates. Detects email provider (Google Workspace, Microsoft 365, Amazon SES, etc.). P0 signals: blacklisted / no MX / TLS expired / no SPF + DMARC none. P1 signals: SPF soft-fail / no DKIM selector / DMARC no reporting. P2 signals: role-based address / TLS expires <30d / domain age <90 days. All checks are keyless (no API keys required). Cache TTL 1h. SLA <=10s p95.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "checks": { "description": "Subset of checks to run. Defaults to all 8: [\"mx\",\"spf\",\"dkim\",\"dmarc\",\"blacklist\",\"whois\",\"tls\",\"reputation\"]. Use a subset for faster responses (e.g. [\"mx\",\"spf\",\"dmarc\",\"reputation\"] for quick scoring).", "items": { "enum": [ "mx", "spf", "dkim", "dmarc", "blacklist", "whois", "tls", "reputation" ], "type": "string" }, "type": "array" }, "domain": { "description": "Domain to check (e.g. \"stripe.com\" or \"@stripe.com\"). If an email address is provided here, the domain is extracted automatically.", "maxLength": 253, "minLength": 3, "type": "string" }, "email": { "description": "Full email address for additional checks: format validity, role-based detection (e.g. \"[email protected]\").", "maxLength": 320, "type": "string" } }, "required": [ "domain" ], "type": "object" }, "name": "email_domain_health_check", "outputSchema": { "properties": { "blacklist": { "properties": { "listed": { "type": "boolean" }, "lists_checked_count": { "type": "number" }, "lists_hit": { "items": { "type": "string" }, "type": "array" } }, "required": [ "listed", "lists_hit", "lists_checked_count" ], "type": "object" }, "deliverability_signals": { "items": { "properties": { "category": { "type": "string" }, "message": { "type": "string" }, "severity": { "enum": [ "P0", "P1", "P2" ], "type": "string" } }, "required": [ "severity", "category", "message" ], "type": "object" }, "type": "array" }, "dkim": { "properties": { "any_valid": { "type": "boolean" }, "records": { "type": "object" }, "selectors_checked": { "items": { "type": "string" }, "type": "array" } }, "required": [ "selectors_checked", "any_valid" ], "type": "object" }, "dmarc": { "properties": { "aggregate_to": { "type": "string" }, "ok": { "type": "boolean" }, "policy": { "enum": [ "none", "quarantine", "reject" ], "type": "string" }, "record": { "type": "string" } }, "required": [ "ok" ], "type": "object" }, "domain": { "type": "string" }, "email_is_role_based": { "type": "boolean" }, "email_valid": { "type": "boolean" }, "mx": { "properties": { "ok": { "type": "boolean" }, "providers_detected": { "items": { "type": "string" }, "type": "array" }, "records": { "items": { "properties": { "host": { "type": "string" }, "priority": { "type": "number" } }, "required": [ "priority", "host" ], "type": "object" }, "type": "array" } }, "required": [ "records", "ok" ], "type": "object" }, "quality_score": { "type": "number" }, "reputation_score": { "type": "number" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "unavailable", "partial" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "spf": { "properties": { "includes": { "items": { "type": "string" }, "type": "array" }, "ok": { "type": "boolean" }, "record": { "type": "string" }, "soft_fail": { "type": "boolean" } }, "required": [ "ok" ], "type": "object" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "tls": { "properties": { "days_remaining": { "type": "number" }, "expires_at": { "type": "string" }, "issuer": { "type": "string" }, "valid": { "type": "boolean" } }, "required": [ "valid" ], "type": "object" }, "whois": { "properties": { "country": { "type": "string" }, "created": { "type": "string" }, "expires": { "type": "string" }, "registrar": { "type": "string" } }, "type": "object" } }, "required": [ "domain", "mx", "spf", "dkim", "dmarc", "blacklist", "reputation_score", "deliverability_signals", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "eNPS automatisé — Gapup agent-payable C-suite expertise (CHRO). Returns a structured, audited deliverable. Reference case: BlaBlaCar — eNPS pulse mensuel · 700 FTE 8 pays · segments × tenure × manager · plays correctifs ciblés. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "currentEnps": { "maximum": 100, "minimum": -100, "type": "number" }, "fte": { "minimum": 20, "type": "integer" }, "locations": { "items": { "type": "string" }, "maxItems": 20, "minItems": 1, "type": "array" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" }, "targetEnps": { "maximum": 100, "minimum": -100, "type": "number" } }, "required": [ "name", "sector", "fte", "locations", "targetEnps" ], "type": "object" }, "context": { "properties": { "knownPainPoints": { "maxLength": 500, "type": "string" }, "recentChanges": { "maxLength": 500, "type": "string" }, "strategicPriorities": { "items": { "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" } }, "required": [ "strategicPriorities" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" }, "presenterScript": { "items": { "properties": { "durationSec": { "maximum": 120, "minimum": 15, "type": "number" }, "keyPointsToAcknowledge": { "items": { "maxLength": 200, "minLength": 5, "type": "string" }, "maxItems": 5, "type": "array" }, "motionHint": { "enum": [ "opening-confidence", "explaining", "pointing-at-chart", "thoughtful-pause", "emphatic-point", "nodding-listening", "hand-gesture-open", "summary-pose", "closing-confidence" ], "type": "string" }, "narration": { "maxLength": 1200, "minLength": 80, "type": "string" }, "slideId": { "maxLength": 60, "minLength": 2, "type": "string" }, "slideTitle": { "maxLength": 120, "minLength": 3, "type": "string" } }, "required": [ "slideId", "slideTitle", "narration", "durationSec" ], "type": "object" }, "maxItems": 15, "minItems": 6, "type": "array" }, "segmentation": { "properties": { "avgTenureMonths": { "minimum": 0, "type": "number" }, "departments": { "items": { "type": "string" }, "maxItems": 20, "minItems": 2, "type": "array" }, "remotePolicy": { "enum": [ "full-remote", "hybrid", "office-first", "office-only" ], "type": "string" }, "tenureBands": { "items": { "type": "string" }, "maxItems": 8, "minItems": 2, "type": "array" } }, "required": [ "departments", "tenureBands", "avgTenureMonths", "remotePolicy" ], "type": "object" }, "toolStack": { "properties": { "bestExisting": { "type": "boolean" }, "hrisName": { "maxLength": 80, "type": "string" }, "surveyTool": { "maxLength": 80, "type": "string" } }, "required": [ "bestExisting" ], "type": "object" } }, "required": [ "company", "segmentation", "context", "toolStack" ], "type": "object" }, "name": "enps_auto", "outputSchema": null }, { "description": "Multi-mode ESG intelligence for ESG analysts, sustainability officers and impact investing fund managers. Aggregates live data from CDP, SBTi, Wikipedia, Yahoo Finance and web search across five modes:\n• company_score — ESG score 0-100 with E/S/G breakdown + heuristic rating (AAA-CCC), from CDP grade + SBTi + sector profile\n• controversy_check — controversies detected via web search, classified P0/P1/P2 by type (greenwashing, emissions fraud, labour, governance)\n• emissions — GHG Scope 1/2/3 estimates, SBTi validation flag, net-zero target year, carbon intensity per M€ revenue\n• esrs_readiness — CSRD gap across 12 standards (E1-E5, S1-S4, G1-G3): readiness % + gap list + CSRD deadline + effort man-days\n• sfdr_classification — suggested SFDR Article 6/8/9 with rationale and sustainability indicators met\n\nSignals: P0=critical (controversy/score<40), P1=significant (score<55/SBTi missing/ESRS<50%), P2=watch. Cache 24h.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "framework": { "description": "ESG framework filter (optional, default: all).", "enum": [ "GRI", "SASB", "TCFD", "ESRS", "all" ], "type": "string" }, "mode": { "description": "Analysis mode.", "enum": [ "company_score", "controversy_check", "emissions", "esrs_readiness", "sfdr_classification" ], "type": "string" }, "pillar": { "description": "ESG pillar filter (optional, default: all).", "enum": [ "E", "S", "G", "all" ], "type": "string" }, "query": { "description": "Company name, ticker, ISIN or LEI (e.g. \"Microsoft\", \"Sanofi\", \"Volkswagen\").", "maxLength": 200, "minLength": 2, "type": "string" } }, "required": [ "mode", "query" ], "type": "object" }, "name": "esg_audit_multi", "outputSchema": { "properties": { "company_score": { "properties": { "breakdown": { "properties": { "E": { "type": "number" }, "G": { "type": "number" }, "S": { "type": "number" } }, "required": [ "E", "S", "G" ], "type": "object" }, "last_update": { "type": "string" }, "methodology": { "type": "string" }, "name": { "type": "string" }, "overall_score": { "type": "number" }, "rating": { "enum": [ "AAA", "AA", "A", "BBB", "BB", "B", "CCC" ], "type": "string" } }, "required": [ "name", "overall_score", "breakdown", "methodology", "last_update" ], "type": "object" }, "controversies": { "items": { "properties": { "category": { "type": "string" }, "date": { "type": "string" }, "evidence_url": { "type": "string" }, "severity": { "enum": [ "P0", "P1", "P2" ], "type": "string" }, "summary": { "type": "string" } }, "required": [ "severity", "category", "summary" ], "type": "object" }, "type": "array" }, "emissions": { "properties": { "intensity_tco2_per_meur_revenue": { "type": "number" }, "last_report_year": { "type": "number" }, "net_zero_target_year": { "type": "number" }, "sbti_validated": { "type": "boolean" }, "scope1_tco2": { "type": "number" }, "scope2_tco2": { "type": "number" }, "scope3_tco2": { "type": "number" } }, "required": [ "scope1_tco2", "scope2_tco2", "sbti_validated", "last_report_year" ], "type": "object" }, "esrs_readiness": { "properties": { "by_standard": { "additionalProperties": { "properties": { "gaps": { "items": { "type": "string" }, "type": "array" }, "ready_pct": { "type": "number" } }, "required": [ "ready_pct", "gaps" ], "type": "object" }, "type": "object" }, "estimated_effort_md": { "type": "number" }, "next_deadline": { "type": "string" }, "overall_readiness_pct": { "type": "number" } }, "required": [ "overall_readiness_pct", "by_standard", "next_deadline", "estimated_effort_md" ], "type": "object" }, "mode": { "type": "string" }, "quality_score": { "type": "number" }, "sfdr_classification": { "properties": { "rationale": { "type": "string" }, "suggested_article": { "enum": [ "6", "8", "9" ], "type": "string" }, "sustainability_indicators_met": { "items": { "type": "string" }, "type": "array" } }, "required": [ "suggested_article", "rationale", "sustainability_indicators_met" ], "type": "object" }, "signals": { "items": { "properties": { "category": { "type": "string" }, "message": { "type": "string" }, "severity": { "enum": [ "P0", "P1", "P2" ], "type": "string" } }, "required": [ "severity", "category", "message" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "partial", "unavailable" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" } }, "required": [ "mode", "signals", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Architecte du narratif ESRS / CSRD — Gapup agent-payable C-suite expertise (SUSTAINABILITY). Returns a structured, audited deliverable. Reference case: L'Oréal France — narratif ESRS E1+E5 + S1 + G1 · CSRD reporting 2025-2026 · double-matérialité chiffrée. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "fte": { "minimum": 1, "type": "integer" }, "name": { "type": "string" }, "reportingYear": { "minimum": 2024, "type": "integer" }, "revenueEur": { "type": "number" }, "sector": { "type": "string" } }, "required": [ "name", "sector", "fte", "revenueEur", "reportingYear" ], "type": "object" }, "context": { "properties": { "recentChanges": { "maxLength": 500, "type": "string" }, "strategicCommitments": { "items": { "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" } }, "required": [ "strategicCommitments" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" }, "presenterScript": { "items": { "properties": { "durationSec": { "maximum": 120, "minimum": 15, "type": "number" }, "keyPointsToAcknowledge": { "items": { "maxLength": 200, "minLength": 5, "type": "string" }, "maxItems": 5, "type": "array" }, "motionHint": { "enum": [ "opening-confidence", "explaining", "pointing-at-chart", "thoughtful-pause", "emphatic-point", "nodding-listening", "hand-gesture-open", "summary-pose", "closing-confidence" ], "type": "string" }, "narration": { "maxLength": 1200, "minLength": 80, "type": "string" }, "slideId": { "maxLength": 60, "minLength": 2, "type": "string" }, "slideTitle": { "maxLength": 120, "minLength": 3, "type": "string" } }, "required": [ "slideId", "slideTitle", "narration", "durationSec" ], "type": "object" }, "maxItems": 15, "minItems": 6, "type": "array" }, "scope": { "properties": { "countriesOfOperation": { "items": { "type": "string" }, "maxItems": 15, "minItems": 1, "type": "array" }, "standardsApplicable": { "items": { "enum": [ "ESRS-E1-climate", "ESRS-E2-pollution", "ESRS-E3-water", "ESRS-E4-biodiversity", "ESRS-E5-resources", "ESRS-S1-workforce", "ESRS-S2-value-chain", "ESRS-S3-communities", "ESRS-S4-consumers", "ESRS-G1-business-conduct" ], "type": "string" }, "maxItems": 10, "minItems": 3, "type": "array" }, "valueChainScope": { "maxLength": 500, "type": "string" } }, "required": [ "standardsApplicable", "countriesOfOperation", "valueChainScope" ], "type": "object" } }, "required": [ "company", "scope", "context" ], "type": "object" }, "name": "esrs_narrative_builder", "outputSchema": null }, { "description": "Marketing événementiel — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Pennylane (€120k/an budget événements) — 7 événements sélectionnés · coût-MQL -38% vs année précédente. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "annualBudgetEur": { "minimum": 0, "type": "number" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "currentEvents": { "items": { "properties": { "name": { "type": "string" }, "roiRating": { "enum": [ "poor", "ok", "good" ], "type": "string" }, "type": { "type": "string" } }, "required": [ "name", "type", "roiRating" ], "type": "object" }, "maxItems": 5, "minItems": 0, "type": "array" }, "geography": { "items": { "maxLength": 60, "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" }, "objectives": { "items": { "enum": [ "pipeline-generation", "brand-awareness", "customer-retention", "recruiting", "partner-network" ], "type": "string" }, "minItems": 1, "type": "array" }, "targetAudience": { "maxLength": 200, "minLength": 5, "type": "string" }, "teamSize": { "maximum": 50, "minimum": 1, "type": "number" } }, "required": [ "company", "targetAudience", "annualBudgetEur", "objectives", "teamSize", "geography", "currentEvents" ], "type": "object" }, "name": "event_marketing", "outputSchema": null }, { "description": "As a Chief Human Resources Officer (CHRO), benchmark executive compensation packages against peer companies using public SEC filings and private compensation data from Equilar and Bloomberg. Inputs include executive name, title, company ticker, and peer group criteria. Outputs structured compensation metrics (base salary, bonus, equity, total compensation) with source attribution and confidence scores.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "companyTicker": { "type": "string" }, "executiveName": { "type": "string" }, "executiveTitle": { "type": "string" }, "fiscalYear": { "type": "number" }, "peerGroup": { "properties": { "customTickers": { "items": { "type": "string" }, "type": "array" }, "industry": { "type": "string" }, "marketCapRange": { "type": "string" }, "revenueRange": { "type": "string" } }, "required": [], "type": "object" } }, "required": [ "executiveName", "executiveTitle", "companyTicker" ], "type": "object" }, "name": "executive_comp_peer_benchmark", "outputSchema": { "properties": { "compensation": { "properties": { "baseSalary": { "type": "number" }, "bonus": { "type": "number" }, "equity": { "type": "number" }, "peerComparison": { "properties": { "count": { "type": "number" }, "percentile25": { "type": "number" }, "percentile50": { "type": "number" }, "percentile75": { "type": "number" } }, "required": [ "count" ], "type": "object" }, "totalCompensation": { "type": "number" } }, "required": [], "type": "object" }, "sources": { "items": { "properties": { "confidence": { "type": "number" }, "fetched_at": { "type": "string" }, "source_type": { "enum": [ "SEC", "Equilar", "Bloomberg" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at", "source_type" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Pure-compute 3-statement financial model builder (Income Statement + Balance Sheet + Cash Flow). Feed assumptions (revenue growth, COGS%, OpEx, CapEx, working capital, tax rate, depreciation, debt schedule) → receive a full 3-5 year projection with integrated DCF valuation. Supports IFRS / US_GAAP / PRC_GAAP (中国会计准则) norms with bilingual ZH+EN labels for PRC. Modes: build (full 3-statement model) | scenario_analysis (base/bull/bear ±20% growth) | sensitivity (1 KPI × 1 input, 5-point grid). No external data needed — all computed from assumptions. ICP: VC due diligence, M&A analysts, CFO SMB, startup founders pitching investors, biotech/SaaS modeling. Returns balance_check_ok per year, DCF enterprise/equity value, and coherence warnings.", "inputSchema": { "properties": { "assumptions": { "description": "Financial assumptions for the model", "properties": { "capex_pct_of_revenue": { "description": "Capital expenditures as % of revenue.", "type": "number" }, "cogs_pct_of_revenue": { "description": "Cost of goods/services as % of revenue (e.g. 30 for SaaS hosting costs).", "type": "number" }, "debt_interest_rate_pct": { "description": "Annual interest rate on debt in %. Default 0.", "type": "number" }, "depreciation_years": { "description": "Useful life of assets in years for straight-line depreciation.", "type": "number" }, "growth_rates_pct": { "description": "Annual revenue growth rates in %, one per year (e.g. [50, 40, 30, 25, 20]). Last value repeated if shorter than period_years.", "items": { "type": "number" }, "type": "array" }, "norms": { "description": "Accounting standard for labels. PRC_GAAP adds 中文 labels alongside EN.", "enum": [ "IFRS", "US_GAAP", "PRC_GAAP" ], "type": "string" }, "opex_pct_of_revenue": { "description": "Operating expenses (R&D + S&M + G&A) as % of revenue.", "type": "number" }, "period_years": { "description": "Projection horizon in years (1-10). Default 5.", "type": "number" }, "starting_cash_eur": { "description": "Opening cash balance in EUR. Default 0.", "type": "number" }, "starting_debt_eur": { "description": "Opening debt balance in EUR. Default 0.", "type": "number" }, "starting_revenue_eur": { "description": "Year 0 (base) annual revenue in EUR. Default 1 000 000.", "type": "number" }, "tax_rate_pct": { "description": "Corporate income tax rate in % (e.g. 25 for France, 21 for US).", "type": "number" }, "working_capital_pct_of_revenue": { "description": "Net working capital as % of revenue.", "type": "number" } }, "required": [ "growth_rates_pct", "cogs_pct_of_revenue", "opex_pct_of_revenue", "capex_pct_of_revenue", "working_capital_pct_of_revenue", "tax_rate_pct", "depreciation_years", "norms" ], "type": "object" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "mode": { "description": "build = full 3-statement model | scenario_analysis = base/bull/bear | sensitivity = 1 KPI × 1 input", "enum": [ "build", "scenario_analysis", "sensitivity" ], "type": "string" }, "sensitivity_input": { "description": "Assumption param to vary in sensitivity mode. E.g. 'growth_rates_pct[0]' or 'cogs_pct_of_revenue'.", "type": "string" }, "sensitivity_kpi": { "description": "KPI to observe in sensitivity mode.", "enum": [ "revenue_y3", "ebitda_y3", "fcf_y3", "cash_y3", "valuation" ], "type": "string" } }, "required": [ "mode", "assumptions" ], "type": "object" }, "name": "financial_model_3statement", "outputSchema": { "properties": { "balance_sheet": { "type": "object" }, "cash_flow": { "type": "object" }, "income_statement": { "type": "object" }, "mode": { "type": "string" }, "norms": { "type": "string" }, "quality_score": { "type": "number" }, "scenarios": { "type": "object" }, "sensitivity": { "type": "array" }, "sources": { "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "valuation_dcf": { "type": "object" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "mode", "norms", "warnings", "quality_score", "status" ], "type": "object" } }, { "description": "Détecteur de fraude — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Reference case: TechManu SAS — Industriel FR €32M CA, 148 FTE · 30j · 21 anomalies · €487k à risque. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "analysisPeriodDays": { "maximum": 365, "minimum": 7, "type": "integer" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "country": { "maxLength": 80, "minLength": 2, "type": "string" }, "fte": { "minimum": 1, "type": "integer" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "revenueEur": { "minimum": 0, "type": "number" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "fte", "revenueEur", "country" ], "type": "object" }, "focus": { "maxLength": 500, "type": "string" }, "transactionVolumes": { "properties": { "bankTransactionsPerMonth": { "minimum": 0, "type": "integer" }, "expenseReportsPerMonth": { "minimum": 0, "type": "integer" }, "supplierInvoicesPerMonth": { "minimum": 0, "type": "integer" }, "totalAmountEur": { "minimum": 0, "type": "number" } }, "required": [ "bankTransactionsPerMonth", "supplierInvoicesPerMonth", "expenseReportsPerMonth", "totalAmountEur" ], "type": "object" } }, "required": [ "company", "analysisPeriodDays", "transactionVolumes" ], "type": "object" }, "name": "fraud_detector", "outputSchema": null }, { "description": "Return vetted, automation-scored business ideas from the FTG idea bank — each with an autonomy score, monetization model and conservative/median/optimistic MRR projections. When to use this tool: an agent or founder wants ranked, buildable business ideas. Input: optional category and limit.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "category": { "description": "Optional category filter", "type": "string" }, "limit": { "maximum": 50, "minimum": 1, "type": "integer" } }, "type": "object" }, "name": "ftg_business_ideas", "outputSchema": { "properties": { "count": { "type": "integer" }, "ideas": { "items": { "type": "object" }, "type": "array" } }, "required": [ "count", "ideas" ], "type": "object" } }, { "description": "Return the business plan for a market-gap opportunity — direct-trade or local-production, with CAPEX, OPEX, ROI, payback period, automation level and the full plan. Cache-first: returns the stored plan when available, otherwise reports that generation is required (the FTG platform produces plans on demand). When to use this tool: an agent has an opportunity_id (from ftg_market_gap) and needs the investable plan. Input: an opportunity_id.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "opportunity_id": { "description": "Opportunity id obtained from ftg_market_gap", "type": "string" } }, "required": [ "opportunity_id" ], "type": "object" }, "name": "ftg_business_plan", "outputSchema": { "properties": { "message": { "type": "string" }, "opportunity_id": { "type": "string" }, "plan_count": { "type": "integer" }, "plans": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "ready", "queued", "not_found" ], "type": "string" } }, "required": [ "opportunity_id", "status" ], "type": "object" } }, { "description": "Return import, trade and production regulations for a country — category, title, summary and source. When to use this tool: an agent checks regulatory or compliance requirements before trading or producing in a market. Input: a country, with an optional category.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "category": { "description": "Optional regulation category filter", "type": "string" }, "country": { "description": "Country ISO code or name", "type": "string" }, "limit": { "maximum": 50, "minimum": 1, "type": "integer" } }, "required": [ "country" ], "type": "object" }, "name": "ftg_country_regulations", "outputSchema": { "properties": { "count": { "type": "integer" }, "regulations": { "items": { "type": "object" }, "type": "array" } }, "required": [ "count", "regulations" ], "type": "object" } }, { "description": "Return the in-depth FTG country study — multi-part structured analysis of a country's trade and production landscape. When to use this tool: an agent needs deep country context before a sourcing, export or investment decision. Input: a country.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "country": { "description": "Country ISO code or name", "type": "string" } }, "required": [ "country" ], "type": "object" }, "name": "ftg_country_study", "outputSchema": { "properties": { "country": { "type": "string" }, "note": { "type": "string" }, "part_count": { "type": "integer" }, "parts": { "items": { "type": "object" }, "type": "array" } }, "required": [ "country", "part_count", "parts" ], "type": "object" } }, { "description": "Return investors from the FTG directory — VC, PE and impact funds with type, firm, website, ticket-size range, sectors and stages of interest. When to use this tool: an agent builds a fundraising shortlist. Input: optional country and limit.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "country": { "description": "Optional country ISO code or name", "type": "string" }, "limit": { "maximum": 50, "minimum": 1, "type": "integer" } }, "type": "object" }, "name": "ftg_investor_directory", "outputSchema": { "properties": { "count": { "type": "integer" }, "investors": { "items": { "type": "object" }, "type": "array" } }, "required": [ "count", "investors" ], "type": "object" } }, { "description": "Return the import/production market-gap opportunities for a country — commodities where local demand outpaces local supply. Each opportunity carries the gap value (USD/year), the gap volume (tonnes/year), a 0-100 opportunity score and the potential margin. When to use this tool: an agent needs to know what a country structurally under-produces or over-imports, for trade sourcing, import/export or local-production investment decisions. Input: a country (ISO-2 code or name).", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "country": { "description": "Country ISO-2 code (e.g. 'SN', 'KE') or name (e.g. 'Senegal')", "type": "string" }, "limit": { "description": "Maximum opportunities to return (default 20)", "maximum": 50, "minimum": 1, "type": "integer" } }, "required": [ "country" ], "type": "object" }, "name": "ftg_market_gap", "outputSchema": { "properties": { "count": { "type": "integer" }, "country": { "type": "string" }, "opportunities": { "items": { "properties": { "category": { "type": [ "string", "null" ] }, "commodity": { "type": [ "string", "null" ] }, "gap_tonnes_year": { "type": [ "number", "null" ] }, "gap_value_usd": { "type": [ "number", "null" ] }, "opportunity_id": { "type": "string" }, "opportunity_score": { "type": [ "number", "null" ] }, "potential_margin_pct": { "type": [ "number", "null" ] }, "summary": { "type": [ "string", "null" ] } }, "type": "object" }, "type": "array" } }, "required": [ "country", "count", "opportunities" ], "type": "object" } }, { "description": "Rank the best countries for a given commodity — where the market gap, opportunity score and potential margin are highest. Cross-country scouting. When to use this tool: an agent has a commodity and needs to know WHERE to sell, export to or set up local production. Input: a commodity name.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "commodity": { "description": "Commodity name (e.g. 'rice', 'soybean', 'poultry')", "minLength": 2, "type": "string" }, "limit": { "description": "Maximum countries to return (default 20)", "maximum": 50, "minimum": 1, "type": "integer" } }, "required": [ "commodity" ], "type": "object" }, "name": "ftg_opportunity_scout", "outputSchema": { "properties": { "commodity": { "type": "string" }, "count": { "type": "integer" }, "countries": { "items": { "properties": { "country": { "type": "string" }, "gap_value_usd": { "type": [ "number", "null" ] }, "iso": { "type": "string" }, "opportunity_score": { "type": [ "number", "null" ] }, "potential_margin_pct": { "type": [ "number", "null" ] } }, "type": "object" }, "type": "array" }, "note": { "type": "string" } }, "required": [ "commodity", "count", "countries" ], "type": "object" } }, { "description": "Return production cost benchmarks (CAPEX/OPEX per unit, value ranges, scenarios, quality tiers) and agronomic yields (t/ha, cycles per year) for a commodity. When to use this tool: an agent sizes the economics of producing a commodity. Input: a commodity, with an optional country.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "commodity": { "description": "Commodity name or slug", "minLength": 2, "type": "string" }, "country": { "description": "Optional country ISO code or name", "type": "string" }, "limit": { "maximum": 50, "minimum": 1, "type": "integer" } }, "required": [ "commodity" ], "type": "object" }, "name": "ftg_production_economics", "outputSchema": { "properties": { "commodity": { "type": "string" }, "cost_benchmarks": { "items": { "type": "object" }, "type": "array" }, "yields": { "items": { "type": "object" }, "type": "array" } }, "required": [ "commodity", "cost_benchmarks", "yields" ], "type": "object" } }, { "description": "Return the production methods for a commodity — each with a description, ordered process steps, pros/cons and a popularity rank. Methods are commodity-canonical: one curated set per commodity, reusable across every country. When to use this tool: an agent evaluates HOW a commodity is produced or processed, compares techniques, or builds a production plan. Input: a commodity slug or name.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "commodity": { "description": "Commodity slug or name (e.g. 'rice', 'tomato', 'cashew')", "minLength": 2, "type": "string" } }, "required": [ "commodity" ], "type": "object" }, "name": "ftg_production_methods", "outputSchema": { "properties": { "commodity": { "type": "string" }, "method_count": { "type": "integer" }, "methods": { "items": { "type": "object" }, "type": "array" }, "note": { "type": "string" } }, "required": [ "commodity", "method_count", "methods" ], "type": "object" } }, { "description": "Return seller catalogues registered on FTG — exporters and producers with their commodity, monthly capacity, certifications and target export markets. When to use this tool: an agent builds a supplier or sourcing shortlist. Input: optional seller country and commodity.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "commodity": { "description": "Optional commodity filter", "type": "string" }, "country": { "description": "Optional seller country ISO code or name", "type": "string" }, "limit": { "maximum": 50, "minimum": 1, "type": "integer" } }, "type": "object" }, "name": "ftg_seller_catalog", "outputSchema": { "properties": { "count": { "type": "integer" }, "sellers": { "items": { "type": "object" }, "type": "array" } }, "required": [ "count", "sellers" ], "type": "object" } }, { "description": "Return verified local buyers in a country — companies sourcing a given commodity, with buyer type, city, website, annual volume range and certification requirements. When to use this tool: an agent builds a sourcing or export shortlist, or needs real B2B demand contacts in a market. Input: a country and an optional commodity filter.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "commodity": { "description": "Optional commodity slug to filter buyers by", "type": "string" }, "country": { "description": "Country ISO-2 code or name", "type": "string" }, "limit": { "description": "Maximum buyers to return (default 20)", "maximum": 50, "minimum": 1, "type": "integer" } }, "required": [ "country" ], "type": "object" }, "name": "ftg_sourcing_buyers", "outputSchema": { "properties": { "buyer_count": { "type": "integer" }, "buyers": { "items": { "type": "object" }, "type": "array" }, "commodity": { "type": [ "string", "null" ] }, "country": { "type": "string" } }, "required": [ "country", "buyer_count", "buyers" ], "type": "object" } }, { "description": "Chasseur de financements — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Reference case: PME deeptech cleantech FR €8M CA — top 30 dispositifs BPI+France2030+EU+VC. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "countryHQ": { "maxLength": 60, "minLength": 2, "type": "string" }, "foundedYear": { "maximum": 2030, "minimum": 1990, "type": "integer" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "pitch": { "maxLength": 800, "minLength": 20, "type": "string" }, "sector": { "maxLength": 120, "minLength": 3, "type": "string" }, "siret": { "type": "string" }, "stage": { "enum": [ "pre-seed", "seed", "series-a", "series-b", "growth" ], "type": "string" } }, "required": [ "name", "pitch", "foundedYear", "countryHQ", "sector", "stage" ], "type": "object" }, "financials": { "properties": { "grossMarginPct": { "maximum": 100, "minimum": 0, "type": "number" }, "hasCIREligibility": { "type": "boolean" }, "hasJEI": { "type": "boolean" }, "headcount": { "minimum": 0, "type": "integer" }, "rdSpendEur": { "minimum": 0, "type": "number" }, "revenueEur": { "minimum": 0, "type": "number" } }, "required": [ "revenueEur", "rdSpendEur", "headcount", "grossMarginPct" ], "type": "object" }, "preferences": { "properties": { "maxDilutionPct": { "maximum": 40, "minimum": 0, "type": "number" }, "nonDilutiveFirst": { "type": "boolean" }, "urgencyDays": { "maximum": 365, "minimum": 30, "type": "integer" } }, "required": [ "nonDilutiveFirst" ], "type": "object" }, "project": { "properties": { "budgetEur": { "maximum": 50000000, "minimum": 50000, "type": "integer" }, "description": { "maxLength": 600, "minLength": 20, "type": "string" }, "geographies": { "items": { "maxLength": 60, "minLength": 2, "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" }, "horizonMonths": { "maximum": 36, "minimum": 6, "type": "integer" }, "techReadinessLevel": { "maximum": 9, "minimum": 1, "type": "integer" } }, "required": [ "description", "budgetEur", "horizonMonths", "geographies" ], "type": "object" } }, "required": [ "company", "financials", "project", "preferences" ], "type": "object" }, "name": "funding_hunter", "outputSchema": null }, { "description": "Get the current or historical foreign-exchange rate for any currency pair — the exact exchange rate, FX rate or conversion rate an agent needs to convert a currency amount or feed a finance, trading, invoicing or pricing workflow. Covers EUR/USD, USD/JPY, GBP/EUR and every ISO-4217 currency pair. Returns the latest spot rate, or a historical rate by date. Use when a workflow needs a precise live or past currency exchange rate, or to convert money between two currencies. Source: European Central Bank reference rates via Frankfurter. Inputs: from/to ISO-4217 currency codes, optional date (YYYY-MM-DD).", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "date": { "description": "Optional YYYY-MM-DD for a historical rate", "type": "string" }, "from": { "description": "Base currency, ISO-4217 (e.g. EUR)", "type": "string" }, "to": { "description": "Quote currency, ISO-4217 (e.g. USD)", "type": "string" } }, "required": [ "from", "to" ], "type": "object" }, "name": "fx_rate", "outputSchema": { "properties": { "as_of": { "type": [ "string", "null" ] }, "from": { "type": "string" }, "rate": { "type": [ "number", "null" ] }, "source": { "type": "string" }, "source_url": { "type": "string" }, "to": { "type": "string" } }, "required": [ "from", "to", "rate", "as_of", "source" ], "type": "object" } }, { "description": "Geospatial logistics intelligence for supply chain, maritime and transport agents. Four modes: (1) geocode_batch — resolve up to 50 addresses to lat/lon with confidence scores (OSM Nominatim + Open-Meteo fallback, 1 req/s rate-limit respected); (2) routing — road/cycling/walking route with distance_km, duration_seconds and ETA ISO timestamp between two addresses or lat/lon points (OSRM public, keyless, global); (3) port_congestion — congestion status for any UN/LOCODE port (e.g. NLRTM, SGSIN, CNSHA) with waiting vessel count, severity (low/medium/high/extreme) and average wait hours; (4) ship_tracking — AIS position, speed, course, destination and ETA for a vessel by its 9-digit MMSI. No API key required for geocode/routing/port. Optional env: AIS_STREAM_API_KEY for live ship data (otherwise MarineTraffic scrape best-effort). SLA: <=25s p95. Cache: 24h geocoding / 1h routing / 30min port / 5min ship. Quality score 0-100. Status: final/partial/failed.", "inputSchema": { "properties": { "addresses": { "description": "geocode_batch only: up to 50 addresses (overrides query if provided)", "items": { "type": "string" }, "maxItems": 50, "type": "array" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "from": { "description": "routing only: origin address or 'lat,lon'", "type": "string" }, "mode": { "description": "'geocode_batch': address -> lat/lon. 'routing': route + ETA. 'port_congestion': UN/LOCODE port state. 'ship_tracking': vessel by MMSI", "enum": [ "geocode_batch", "routing", "port_congestion", "ship_tracking" ], "type": "string" }, "mode_transport": { "description": "routing only: transport mode. Default: driving", "enum": [ "driving", "walking", "cycling" ], "type": "string" }, "query": { "description": "Primary input: address for geocode/routing, UN/LOCODE (e.g. NLRTM) for port_congestion, 9-digit MMSI for ship_tracking", "type": "string" }, "to": { "description": "routing only: destination address or 'lat,lon'", "type": "string" } }, "required": [ "mode", "query" ], "type": "object" }, "name": "geo_logistics_intel", "outputSchema": { "properties": { "geocode_batch": { "items": { "properties": { "confidence": { "type": "number" }, "country_iso": { "type": "string" }, "input": { "type": "string" }, "lat": { "type": "number" }, "lon": { "type": "number" }, "resolved_name": { "type": "string" } }, "required": [ "input", "confidence" ], "type": "object" }, "type": "array" }, "mode": { "type": "string" }, "port_congestion": { "properties": { "average_wait_hours": { "type": "number" }, "country": { "type": "string" }, "last_update": { "type": "string" }, "port_code": { "type": "string" }, "port_name": { "type": "string" }, "severity": { "enum": [ "low", "medium", "high", "extreme" ], "type": "string" }, "waiting_vessels": { "type": "number" } }, "required": [ "port_code", "port_name", "country", "waiting_vessels", "last_update", "severity" ], "type": "object" }, "quality_score": { "type": "number" }, "routing": { "properties": { "distance_km": { "type": "number" }, "duration_seconds": { "type": "number" }, "eta_iso": { "type": "string" }, "geometry": {}, "transport": { "type": "string" } }, "required": [ "distance_km", "duration_seconds", "eta_iso", "transport" ], "type": "object" }, "ship_tracking": { "properties": { "course_deg": { "type": "number" }, "destination_port": { "type": "string" }, "eta": { "type": "string" }, "flag": { "type": "string" }, "last_position": { "properties": { "lat": { "type": "number" }, "lon": { "type": "number" }, "ts": { "type": "string" } }, "required": [ "lat", "lon", "ts" ], "type": "object" }, "mmsi": { "type": "number" }, "name": { "type": "string" }, "speed_knots": { "type": "number" }, "type": { "type": "string" } }, "required": [ "mmsi" ], "type": "object" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "partial", "unavailable" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" } }, "required": [ "mode", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Expansion géographique — Gapup agent-payable C-suite expertise (CSO). Returns a structured, audited deliverable. Reference case: Gapup Hub — Expansion 4 marchés (DE/UK/ES/NL) · €1.8M budget · ARR cible €3.2M Y2. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "currentMarkets": { "items": { "maxLength": 50, "type": "string" }, "maxItems": 10, "type": "array" }, "headcount": { "minimum": 0, "type": "integer" }, "name": { "maxLength": 100, "type": "string" }, "sector": { "maxLength": 100, "type": "string" } }, "required": [ "name", "sector", "currentMarkets" ], "type": "object" }, "constraints": { "items": { "maxLength": 200, "type": "string" }, "maxItems": 5, "type": "array" }, "expansionHorizonMonths": { "maximum": 60, "minimum": 6, "type": "integer" }, "financials": { "properties": { "currentArrEur": { "minimum": 0, "type": "number" }, "expansionBudgetEur": { "minimum": 0, "type": "number" }, "targetArrNewMarketsEur": { "minimum": 0, "type": "number" } }, "required": [ "currentArrEur", "expansionBudgetEur" ], "type": "object" }, "preferredEntryMode": { "enum": [ "direct-sales", "partnership", "acquisition", "distributor", "greenfield" ], "type": "string" }, "product": { "properties": { "avgDealValueEur": { "minimum": 0, "type": "number" }, "name": { "maxLength": 100, "type": "string" }, "pricingModel": { "maxLength": 100, "type": "string" } }, "required": [ "name" ], "type": "object" }, "targetMarkets": { "items": { "properties": { "country": { "maxLength": 80, "type": "string" }, "gdpPerCapitaUsd": { "minimum": 0, "type": "number" }, "languages": { "items": { "maxLength": 30, "type": "string" }, "maxItems": 5, "type": "array" }, "population": { "minimum": 0, "type": "number" }, "region": { "maxLength": 80, "type": "string" }, "regulatoryNotes": { "maxLength": 300, "type": "string" } }, "required": [ "country" ], "type": "object" }, "maxItems": 8, "minItems": 1, "type": "array" } }, "required": [ "company", "product", "targetMarkets" ], "type": "object" }, "name": "geographic_expansion", "outputSchema": null }, { "description": "GL Reconciler — Réconciliation grand livre — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Answers: Identify the root causes of the <N> GL breaks in <company>'s ledger for <period> — cluster them and rank by materiality. · For <company> Q<X> close: which accounts have unreconciled items over €<threshold>? Provide a sign-off routing and resolution plan. · Run an automated GL reconciliation for <company> — AR/AP/intercompany entries — flag open items, suggest journal entries. · What are the top 5 systemic control weaknesses causing recurring GL breaks at <company>? Recommend preventive controls. · Generate a month-end close reconciliation report for <company> <period> — breaks by account type, aging analysis, sign-off assignments. Reference case: Acme SaaS Q4 2026 — 47 breaks GL, €1.4M variance non postée. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "entity": { "properties": { "currency": { "default": "EUR", "type": "string" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "period": { "maxLength": 30, "minLength": 4, "type": "string" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "currency", "period" ], "type": "object" }, "focus": { "maxLength": 500, "type": "string" }, "ledgerContext": { "properties": { "breaksCount": { "minimum": 0, "type": "integer" }, "modules": { "items": { "type": "string" }, "minItems": 1, "type": "array" }, "notes": { "maxLength": 2000, "type": "string" }, "totalEntries": { "minimum": 1, "type": "integer" } }, "required": [ "totalEntries", "breaksCount", "modules" ], "type": "object" } }, "required": [ "entity", "ledgerContext" ], "type": "object" }, "name": "gl_reconciler", "outputSchema": null }, { "description": "Adjusts salary benchmarks for local inflation using OECD, IMF, and World Bank data. Designed for CHROs to normalize compensation across regions with accurate inflation adjustments. Inputs include country codes, base salary, and reference year. Outputs inflation-adjusted salary with data sources and warnings.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "baseSalary": { "minimum": 0, "type": "number" }, "countryCode": { "pattern": "^[A-Z]{2}$", "type": "string" }, "referenceYear": { "maximum": 2026, "minimum": 1990, "type": "number" }, "targetYear": { "maximum": 2026, "minimum": 1990, "type": "number" } }, "required": [ "countryCode", "baseSalary", "referenceYear" ], "type": "object" }, "name": "global_salary_inflation_adjuster", "outputSchema": { "properties": { "adjustedSalary": { "type": "number" }, "countryCode": { "type": "string" }, "inflationRate": { "type": "number" }, "referenceYear": { "type": "number" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "targetYear": { "type": "number" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Aggregate public procurement tenders (calls for tender / appels d'offres) from multiple government sources simultaneously: TED Europa v3 (27 EU countries, keyless API), BOAMP France (opendatasoft, keyless), UK Contracts Finder (OCDS standard, keyless), SAM.gov United States (requires SAM_GOV_API_KEY env var), and bund.de Germany (HTML scraping, partial). Returns structured tender records with buyer authority, EU CPV sector code, estimated contract value converted to EUR via live FX rates, submission deadlines, and direct notice URLs. Use when: a B2G agent needs to find government contract opportunities matching keywords across multiple jurisdictions; building a pipeline of public tenders for bid/no-bid qualification; monitoring a domain by CPV code; market sizing public sector spend. Key inputs: query (keywords), countries (ISO-2 array), cpv_codes (EU standard codes, e.g. 72000000=IT services, 45000000=construction, 79000000=business services), min_value_eur (filter), published_after (ISO date, defaults to 30 days ago). SLA: <=25s p95 (all sources fetched in parallel, 8s budget per source). Optional env var SAM_GOV_API_KEY enables US federal tenders (free key at api.sam.gov). Quality score: 25 pts if TED EU retrieved, 15 pts per other source retrieved (max 60), 10 pts if >= 10 tenders returned, 5 pts if aggregates computed. Status: failed < 30 / partial 30-59 / final >= 60.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "countries": { "description": "Countries to search. Defaults to [\"EU\",\"US\",\"FR\",\"UK\",\"DE\"]. Use \"EU\" for all 27 EU member states via TED Europa.", "items": { "enum": [ "EU", "US", "FR", "UK", "DE", "JP", "BR" ], "type": "string" }, "type": "array" }, "cpv_codes": { "description": "EU Common Procurement Vocabulary codes (e.g. ['72000000'] for IT services, ['45000000'] for construction). Optional.", "items": { "type": "string" }, "type": "array" }, "min_value_eur": { "description": "Minimum contract value in EUR. Tenders below this are excluded. Optional.", "type": "number" }, "published_after": { "description": "ISO date YYYY-MM-DD. Only return tenders published after this date. Defaults to 30 days ago.", "type": "string" }, "query": { "description": "Keywords to search for tenders (e.g. \"cybersecurity audit\", \"construction\", \"consulting AI\")", "maxLength": 200, "minLength": 2, "type": "string" } }, "required": [ "query" ], "type": "object" }, "name": "gov_procurement_multi", "outputSchema": { "properties": { "by_country": { "additionalProperties": { "properties": { "count": { "type": "number" }, "total_value_eur": { "type": "number" } }, "required": [ "count", "total_value_eur" ], "type": "object" }, "type": "object" }, "by_source": { "additionalProperties": { "properties": { "count": { "type": "number" }, "status": { "enum": [ "retrieved", "partial", "unavailable" ], "type": "string" } }, "required": [ "count", "status" ], "type": "object" }, "type": "object" }, "countries_searched": { "items": { "type": "string" }, "type": "array" }, "quality_score": { "type": "number" }, "query": { "type": "string" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "partial", "unavailable" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "tenders": { "items": { "properties": { "buyer": { "type": "string" }, "country": { "type": "string" }, "cpv_main": { "type": "string" }, "deadline": { "type": "string" }, "notice_id": { "type": "string" }, "procedure_type": { "type": "string" }, "publication_date": { "type": "string" }, "sector": { "type": "string" }, "snippet": { "type": "string" }, "source": { "enum": [ "TED_EU", "SAM_GOV", "BOAMP_FR", "UK_GOV", "BUND_DE", "EGOV_JP", "COMPRASNET_BR" ], "type": "string" }, "title": { "type": "string" }, "url": { "type": "string" }, "value_eur": { "type": "number" }, "value_local": { "properties": { "amount": { "type": "number" }, "currency": { "type": "string" } }, "type": "object" } }, "required": [ "country", "source", "notice_id", "title", "buyer", "publication_date", "url" ], "type": "object" }, "type": "array" } }, "required": [ "query", "countries_searched", "tenders", "by_country", "by_source", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Architecte de croissance — Gapup agent-payable C-suite expertise (CSO). Returns a structured, audited deliverable. Reference case: Pennylane (€30M ARR) — 3 voies de croissance · Mix recommandé : Organique + Geo EU · ARR cible €120M en 36 mois. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "currentArrEur": { "minimum": 0, "type": "number" }, "geographies": { "items": { "maxLength": 60, "type": "string" }, "maxItems": 6, "minItems": 1, "type": "array" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" }, "teamSize": { "maximum": 10000, "minimum": 1, "type": "number" } }, "required": [ "name", "sector", "currentArrEur", "geographies", "teamSize" ], "type": "object" }, "constraints": { "properties": { "geographicExpansionReadiness": { "enum": [ "not-ready", "6-12mo", "ready-now" ], "type": "string" }, "maAppetite": { "enum": [ "none", "opportunistic", "active" ], "type": "string" }, "productExpansionBudgetEur": { "minimum": 0, "type": "number" } }, "required": [ "maAppetite", "geographicExpansionReadiness" ], "type": "object" }, "currentDrivers": { "items": { "properties": { "contributionPct": { "maximum": 100, "minimum": 0, "type": "number" }, "driver": { "maxLength": 100, "minLength": 2, "type": "string" }, "growthRatePct": { "maximum": 300, "minimum": -50, "type": "number" } }, "required": [ "driver", "contributionPct", "growthRatePct" ], "type": "object" }, "maxItems": 6, "minItems": 1, "type": "array" }, "growthTarget": { "properties": { "budgetAvailableEur": { "minimum": 0, "type": "number" }, "horizonMonths": { "maximum": 60, "minimum": 6, "type": "number" }, "targetArrEur": { "minimum": 0, "type": "number" } }, "required": [ "targetArrEur", "horizonMonths", "budgetAvailableEur" ], "type": "object" } }, "required": [ "company", "growthTarget", "currentDrivers", "constraints" ], "type": "object" }, "name": "growth_path_architect", "outputSchema": null }, { "description": "Evaluates the likelihood of hallucination in LLM responses by comparing against HuggingFace model confidence scores. Designed for risk assessment personas to quantify response reliability. Accepts text snippets or model outputs, returns confidence metrics and potential hallucination warnings. Cross-references with top-performing models from the HuggingFace leaderboard.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "model_id": { "description": "Optional specific HuggingFace model ID to use for evaluation", "type": "string" }, "text": { "description": "The LLM-generated text to evaluate for hallucination risk", "type": "string" }, "threshold": { "default": 0.7, "description": "Confidence threshold below which hallucination warnings are triggered", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "text" ], "type": "object" }, "name": "hallucination_confidence_meter", "outputSchema": { "properties": { "confidence_scores": { "items": { "properties": { "model_id": { "type": "string" }, "score": { "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "model_id", "score" ], "type": "object" }, "type": "array" }, "hallucination_likelihood": { "maximum": 1, "minimum": 0, "type": "number" }, "sources": { "items": { "properties": { "confidence_score": { "maximum": 1, "minimum": 0, "type": "number" }, "fetched_at": { "format": "date-time", "type": "string" }, "model_id": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Fetch historical OHLCV price series for any ticker: stocks (AAPL, SAP.DE, 7203.T), ETFs, indices, commodities (GC=F for gold) or cryptocurrencies (BTC-USD). Returns a full date-indexed series of open/high/low/close/volume plus pre-computed statistics: total return, annualised return (CAGR), annualised volatility, max drawdown and Sharpe estimate (rf=4%). Automatically detects crypto tickers (→ CoinGecko) vs traditional assets (→ Yahoo Finance primary, Stooq fallback). Adjusts for dividends and splits when adjusted=true (default). Use cases: backtesting, factor analysis, performance attribution, charting, financial modelling. Sources: Yahoo Finance, CoinGecko, Stooq. All keyless. Optional env: AICI_RESEARCH_PROXY_URL for Bright Data routing (lifts Yahoo 429), TWELVE_DATA_API_KEY for higher Twelve Data quota.", "inputSchema": { "properties": { "adjusted": { "description": "Adjust close prices for dividends and splits. Default: true.", "type": "boolean" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "interval": { "description": "Bar interval. Default: 1d (daily).", "enum": [ "1d", "1wk", "1mo" ], "type": "string" }, "metrics": { "description": "Subset of fields to include (informational — all fields always returned).", "items": { "enum": [ "open", "high", "low", "close", "volume", "returns", "volatility" ], "type": "string" }, "type": "array" }, "period": { "description": "Look-back period. Default: 1y.", "enum": [ "1mo", "3mo", "6mo", "1y", "2y", "5y", "10y", "max" ], "type": "string" }, "ticker": { "description": "Yahoo Finance ticker symbol. Examples: AAPL (US stock), SAP.DE (Frankfurt), 7203.T (Tokyo), BTC-USD (Bitcoin), GC=F (gold futures), ^GSPC (S&P 500).", "type": "string" } }, "required": [ "ticker" ], "type": "object" }, "name": "historical_price_series", "outputSchema": { "properties": { "currency": { "type": "string" }, "data_points": { "type": "number" }, "dividends_detected": { "items": { "properties": { "amount": { "type": "number" }, "date": { "type": "string" }, "yield_pct": { "type": "number" } }, "required": [ "date", "amount" ], "type": "object" }, "type": "array" }, "interval": { "type": "string" }, "period": { "type": "string" }, "quality_score": { "type": "number" }, "resolved_exchange": { "type": "string" }, "series": { "items": { "properties": { "adjusted_close": { "type": "number" }, "close": { "type": "number" }, "date": { "type": "string" }, "high": { "type": "number" }, "low": { "type": "number" }, "open": { "type": "number" }, "volume": { "type": "number" } }, "required": [ "date", "close" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "partial", "unavailable" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "splits_detected": { "items": { "properties": { "date": { "type": "string" }, "ratio": { "type": "number" } }, "required": [ "date", "ratio" ], "type": "object" }, "type": "array" }, "stats": { "properties": { "days_returned": { "type": "number" }, "max_drawdown_pct": { "type": "number" }, "return_annualised_pct": { "type": "number" }, "return_total_pct": { "type": "number" }, "sharpe_estimate": { "type": [ "number", "null" ] }, "volatility_annualised_pct": { "type": "number" } }, "required": [ "return_total_pct", "return_annualised_pct", "volatility_annualised_pct", "max_drawdown_pct", "days_returned" ], "type": "object" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "ticker": { "type": "string" } }, "required": [ "ticker", "currency", "interval", "period", "data_points", "series", "stats", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Asynchronous tool for Chief Human Resources Officers (CHROs) to align employee benefits packages with ESG (Environmental, Social, Governance) goals. Uses Eurostat HR data, MSCI ESG ratings, and Sustainalytics metrics to generate actionable recommendations. Inputs include company location, industry, and current benefits structure. Outputs ESG-aligned benefits adjustments with sustainability impact scores. Requires async:true to avoid timeout errors.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "companyLocation": { "description": "ISO 2-letter country code of company headquarters", "type": "string" }, "currentBenefits": { "items": { "properties": { "annualCost": { "type": "number" }, "benefitType": { "type": "string" }, "description": { "type": "string" } }, "required": [ "benefitType", "annualCost" ], "type": "object" }, "type": "array" }, "esgFocus": { "description": "Primary ESG pillars to prioritize", "items": { "enum": [ "Environmental", "Social", "Governance" ], "type": "string" }, "type": "array" }, "industryCode": { "description": "NACE or ISIC industry classification code", "type": "string" } }, "required": [ "companyLocation", "industryCode", "currentBenefits" ], "type": "object" }, "name": "hr_benefits_esg_aligner", "outputSchema": { "properties": { "overallESGAlignmentScore": { "maximum": 100, "minimum": 0, "type": "number" }, "recommendations": { "items": { "properties": { "benefitType": { "type": "string" }, "currentCost": { "type": "number" }, "esgImpactScore": { "maximum": 100, "minimum": 0, "type": "number" }, "implementationCost": { "type": "number" }, "proposedAdjustment": { "type": "string" }, "sustainabilityMetric": { "type": "string" } }, "required": [ "benefitType", "esgImpactScore", "sustainabilityMetric" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "dataType": { "type": "string" }, "fetched_at": { "format": "date-time", "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "As a CTO, gather forensic evidence (logs, network flows, MITRE TTPs) from public breach reports and threat intelligence sources to support incident response post-mortems. Inputs include incident identifiers, date ranges, or MITRE technique IDs. Outputs structured evidence with attack patterns, indicators of compromise, and source references. — pass async:true REQUIRED to avoid x402 timeout.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "date_range": { "properties": { "end": { "format": "date-time", "type": "string" }, "start": { "format": "date-time", "type": "string" } }, "type": "object" }, "incident_id": { "description": "Unique identifier for the incident (e.g., CVE, GitHub Advisory ID)", "type": "string" }, "include_network_flows": { "default": false, "type": "boolean" }, "mitre_technique_ids": { "description": "List of MITRE ATT&CK technique IDs (e.g., T1059)", "items": { "type": "string" }, "type": "array" } }, "required": [ "incident_id" ], "type": "object" }, "name": "incident_response_evidence_collector", "outputSchema": { "properties": { "attack_patterns": { "items": { "properties": { "description": { "type": "string" }, "mitigations": { "items": { "type": "string" }, "type": "array" }, "tactic": { "type": "string" }, "technique_id": { "type": "string" } }, "type": "object" }, "type": "array" }, "incident_id": { "type": "string" }, "indicators": { "items": { "properties": { "description": { "type": "string" }, "type": { "type": "string" }, "value": { "type": "string" } }, "type": "object" }, "type": "array" }, "network_flows": { "items": { "properties": { "bytes": { "type": "number" }, "destination_ip": { "type": "string" }, "protocol": { "type": "string" }, "source_ip": { "type": "string" }, "timestamp": { "format": "date-time", "type": "string" } }, "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "source_type": { "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "timeline": { "items": { "properties": { "event": { "type": "string" }, "source": { "type": "string" }, "timestamp": { "format": "date-time", "type": "string" } }, "type": "object" }, "type": "array" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Indian capital market intelligence for the IN diaspora (30M+) and investors. Covers NSE, BSE, and MCA corporate registry across four modes:\n\n• company — full company profile: name, CIN, exchange, NSE/BSE tickers, industry, incorporation date, paid-up capital, registered office, status, directors\n• market_quote — real-time quote: price (INR), change%, volume, market cap, P/E ratio. Sources: Yahoo Finance (primary), BSE API, NSE API (proxy-gated)\n• sector_overview — Nifty/Sensex sector snapshot: top 5 companies by market cap. Supported sectors: it, banking, pharma, energy, auto, fmcg, realestate, metals, telecom, consumer\n• mca_filing — Ministry of Corporate Affairs filings. Requires CIN for direct lookup.\n\nInput formats accepted:\n • NSE ticker (e.g. 'RELIANCE', 'TCS.NS')\n • BSE 6-digit code (e.g. '500325' for Reliance)\n • CIN 21-char (e.g. 'L17110MH1973PLC019786')\n • Company name EN (e.g. 'Reliance Industries', 'Tata Consultancy')\n • Sector keyword (e.g. 'IT services', 'banking', 'pharma')\n\nENV: AICI_RESEARCH_PROXY_URL with country-in routing unlocks NSE direct API and MCA.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "exchange": { "description": "Exchange filter. Default: all.", "enum": [ "NSE", "BSE", "all" ], "type": "string" }, "mode": { "description": "Analysis mode.", "enum": [ "company", "market_quote", "sector_overview", "mca_filing" ], "type": "string" }, "query": { "description": "NSE/BSE ticker, CIN (21 chars), company name (EN), or sector keyword.", "maxLength": 200, "minLength": 1, "type": "string" } }, "required": [ "mode", "query" ], "type": "object" }, "name": "india_market_data", "outputSchema": { "properties": { "company": { "properties": { "cin": { "type": "string" }, "directors": { "items": { "properties": { "din": { "type": "string" }, "name": { "type": "string" }, "role": { "type": "string" } }, "required": [ "name", "din", "role" ], "type": "object" }, "type": "array" }, "exchange": { "enum": [ "NSE", "BSE" ], "type": "string" }, "incorporated": { "type": "string" }, "industry": { "type": "string" }, "name": { "type": "string" }, "paid_up_capital_inr": { "type": "number" }, "registered_office": { "type": "string" }, "status": { "type": "string" }, "ticker_bse": { "type": "string" }, "ticker_nse": { "type": "string" } }, "required": [ "name" ], "type": "object" }, "market_quote": { "properties": { "change_pct": { "type": "number" }, "exchange": { "type": "string" }, "last_update": { "type": "string" }, "market_cap_inr": { "type": "number" }, "pe": { "type": "number" }, "price_inr": { "type": "number" }, "ticker": { "type": "string" }, "volume": { "type": "number" } }, "required": [ "ticker", "exchange", "price_inr", "change_pct", "volume", "last_update" ], "type": "object" }, "mca_filings": { "items": { "properties": { "date": { "type": "string" }, "source": { "type": "string" }, "title": { "type": "string" }, "type": { "type": "string" }, "url": { "type": "string" } }, "required": [ "date", "type", "title", "url", "source" ], "type": "object" }, "type": "array" }, "mode": { "type": "string" }, "quality_score": { "type": "number" }, "query": { "type": "string" }, "sector_overview": { "properties": { "sector": { "type": "string" }, "sector_index_change_30d": { "type": "number" }, "top_5_companies": { "items": { "properties": { "market_cap": { "type": "number" }, "name": { "type": "string" }, "ticker": { "type": "string" } }, "required": [ "name", "ticker" ], "type": "object" }, "type": "array" } }, "required": [ "sector", "top_5_companies" ], "type": "object" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "partial", "unavailable" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" } }, "required": [ "mode", "query", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Classificateur d'industrie NAICS/SIC/NACE — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Answers: What is the NAICS code for a company that does <activity>? · Give me NAICS + SIC + NACE classification for this company description. · Which industry sector (GICS) does this company belong to for equity analysis? · What HS code applies to products manufactured by this company? · For EU procurement compliance, what NACE Rev. 2 code applies to this company? · Classify this business into NAICS + SIC + ISIC + GICS + NACE + HS with hierarchy and confidence. · I need to segment my ICP list by NAICS 4-digit subsector — classify these company descriptions. Reference case: Helios Cold Chain EU — Freight forwarding maritime réfrigéré · . Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company_description": { "maxLength": 2000, "minLength": 30, "type": "string" }, "company_name": { "maxLength": 200, "type": "string" }, "company_url": { "format": "uri", "type": "string" }, "focus_classifications": { "items": { "enum": [ "naics", "sic", "isic", "gics", "nace", "hs" ], "type": "string" }, "maxItems": 6, "minItems": 1, "type": "array" }, "primary_revenue_source": { "maxLength": 500, "type": "string" } }, "required": [ "company_description" ], "type": "object" }, "name": "industry_classifier_naics_sic", "outputSchema": null }, { "description": "Architecte infra cloud — Gapup agent-payable C-suite expertise (CTO). Returns a structured, audited deliverable. Answers: Design a cloud infrastructure blueprint for a <workload_type> app with <load> expected traffic and <compliance> requirements. · What is the recommended AWS vs GCP vs Azure architecture for a SaaS multi-tenant app with EU data residency and SOC2? · How should I architect my cloud infra to stay under €5k/month with GDPR compliance and a junior DevOps team? · What cloud services do I need for a <workload_type> with <load> load — compute, DB, cache, CDN, observability? · Give me an end-to-end cloud architecture with scaling plan, security baseline, and IaC tool recommendation. Reference case: Spinora fintech B2B SaaS — saas-multi-tenant · medium load (1k-100k req/d) · eu-west · . Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "budget_monthly_eur": { "maximum": 1000000, "minimum": 0, "type": "number" }, "business_context": { "maxLength": 800, "minLength": 20, "type": "string" }, "cloud_preference": { "items": { "enum": [ "aws", "gcp", "azure", "cloud-agnostic" ], "type": "string" }, "maxItems": 4, "minItems": 1, "type": "array" }, "compliance_required": { "items": { "enum": [ "hipaa", "soc2", "pci-dss", "gdpr", "iso27001", "fedramp" ], "type": "string" }, "maxItems": 6, "type": "array" }, "expected_load": { "enum": [ "low (<1k req/d)", "medium (1k-100k req/d)", "high (100k-10M req/d)", "massive (10M+ req/d)" ], "type": "string" }, "region_preference": { "enum": [ "us-east", "us-west", "eu-west", "eu-north", "apac", "multi-region" ], "type": "string" }, "team_size": { "maximum": 500, "minimum": 1, "type": "integer" }, "workload_type": { "enum": [ "saas-multi-tenant", "web-app-monolith", "api-microservices", "data-pipeline-etl", "ml-training", "ml-inference", "static-website", "mobile-backend", "iot-stream", "video-streaming" ], "type": "string" } }, "required": [ "workload_type", "expected_load", "region_preference" ], "type": "object" }, "name": "infra_blueprint_designer", "outputSchema": null }, { "description": "Analyseur de couvertures d'assurance — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Reference case: Gapup Hub — 3 polices · €24k prime · Score 58/100 · 3 gaps critiques · RFP template. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "arrEur": { "type": "number" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "companyName": { "type": "string" }, "currentPolicies": { "items": { "properties": { "annualPremiumEur": { "type": "number" }, "coverageLimitEur": { "type": "number" }, "deductibleEur": { "type": "number" }, "expiryDate": { "type": "string" }, "insurer": { "type": "string" }, "keyExclusions": { "items": { "type": "string" }, "maxItems": 5, "type": "array" }, "type": { "enum": [ "do", "cyber", "rc-pro", "eo", "keyman", "property", "employment-practices", "product-liability" ], "type": "string" } }, "required": [ "type", "insurer", "annualPremiumEur", "coverageLimitEur", "deductibleEur", "expiryDate", "keyExclusions" ], "type": "object" }, "maxItems": 10, "minItems": 1, "type": "array" }, "employeeCount": { "type": "number" }, "jurisdiction": { "type": "string" }, "objectives": { "items": { "type": "string" }, "maxItems": 4, "minItems": 1, "type": "array" }, "riskProfile": { "properties": { "hasBoardOfDirectors": { "type": "boolean" }, "hasInternationalOps": { "type": "boolean" }, "hasKeyPersonDependency": { "type": "boolean" }, "hasPersonalData": { "type": "boolean" }, "hasPhysicalProducts": { "type": "boolean" }, "seriesStage": { "type": "string" } }, "required": [ "hasPersonalData", "hasBoardOfDirectors", "hasInternationalOps", "hasPhysicalProducts", "hasKeyPersonDependency", "seriesStage" ], "type": "object" }, "sector": { "type": "string" } }, "required": [ "companyName", "arrEur", "employeeCount", "sector", "jurisdiction", "currentPolicies", "riskProfile", "objectives" ], "type": "object" }, "name": "insurance_coverage_analyzer", "outputSchema": null }, { "description": "Return a precise reference interest rate — the exact figure an agent injects into a treasury, lending, valuation or trading model. Available rates: fed_funds, sofr, us_10y, us_2y, us_3m, ecb_main, euribor_3m. Source: FRED (Federal Reserve Bank of St. Louis). When to use: an agent's computation needs a current benchmark rate as a precise input.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "rate": { "description": "Reference rate name", "enum": [ "fed_funds", "sofr", "us_10y", "us_2y", "us_3m", "ecb_main", "euribor_3m" ], "type": "string" } }, "required": [ "rate" ], "type": "object" }, "name": "interest_rate", "outputSchema": { "properties": { "as_of": { "type": [ "string", "null" ] }, "rate": { "type": "string" }, "series_id": { "type": "string" }, "source": { "type": "string" }, "source_url": { "type": "string" }, "unit": { "type": "string" }, "value": { "type": [ "number", "null" ] } }, "required": [ "rate", "value", "unit", "as_of", "source" ], "type": "object" } }, { "description": "Communication interne — Gapup agent-payable C-suite expertise (CHRO). Returns a structured, audited deliverable. Reference case: Cas démo — Communication interne. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "audienceSegments": { "items": { "properties": { "name": { "type": "string" }, "notes": { "maxLength": 200, "type": "string" }, "size": { "minimum": 0, "type": "integer" } }, "required": [ "name", "size" ], "type": "object" }, "maxItems": 8, "minItems": 2, "type": "array" }, "company": { "properties": { "fte": { "minimum": 0, "type": "integer" }, "geographies": { "items": { "maxLength": 60, "minLength": 2, "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "nbSites": { "minimum": 0, "type": "integer" }, "sector": { "maxLength": 80, "minLength": 2, "type": "string" }, "stage": { "maxLength": 40, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "fte", "stage" ], "type": "object" }, "context": { "properties": { "currentMaturity": { "type": "string" }, "knownGaps": { "items": { "maxLength": 200, "minLength": 5, "type": "string" }, "maxItems": 8, "minItems": 1, "type": "array" }, "upcomingChanges": { "items": { "maxLength": 200, "minLength": 5, "type": "string" }, "maxItems": 8, "type": "array" }, "voiceTone": { "maxLength": 200, "minLength": 5, "type": "string" } }, "required": [ "currentMaturity", "knownGaps", "voiceTone" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" } }, "required": [ "company", "context", "audienceSegments" ], "type": "object" }, "name": "internal_communication", "outputSchema": null }, { "description": "Liste d'investisseurs + warm intros — Gapup agent-payable C-suite expertise (FUNDRAISING). Returns a structured, audited deliverable. Reference case: Agicap Série D — 25 VCs matchés · Tier A: Balderton/Accel/Partech · Warm intro path chaque investisseur. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "existingInvestors": { "items": { "maxLength": 100, "type": "string" }, "maxItems": 5, "minItems": 0, "type": "array" }, "round": { "properties": { "arrCurrent": { "minimum": 0, "type": "number" }, "geography": { "items": { "maxLength": 50, "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" }, "stage": { "enum": [ "pre-seed", "seed", "series-a", "series-b", "series-c", "growth", "pre-ipo" ], "type": "string" }, "ticketSizeMax": { "minimum": 0, "type": "number" }, "ticketSizeMin": { "minimum": 0, "type": "number" }, "useCase": { "maxLength": 300, "minLength": 10, "type": "string" } }, "required": [ "stage", "ticketSizeMin", "ticketSizeMax", "geography", "useCase" ], "type": "object" } }, "required": [ "company", "round" ], "type": "object" }, "name": "investor_list", "outputSchema": null }, { "description": "Shortlist d'investisseurs ciblés — Gapup agent-payable C-suite expertise (FUNDRAISING). Returns a structured, audited deliverable. Reference case: Aleph AI — Series B €30M · 60 investisseurs EU/US matchés par stage/thèse · fit score + warm intro path + first message angle. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "arrCurrentEur": { "minimum": 0, "type": "number" }, "description": { "maxLength": 500, "minLength": 20, "type": "string" }, "foundedYear": { "maximum": 2026, "minimum": 2000, "type": "integer" }, "fte": { "minimum": 1, "type": "integer" }, "hqCountry": { "maxLength": 80, "minLength": 2, "type": "string" }, "keyMetrics": { "maxLength": 400, "type": "string" }, "monthlyGrowthPct": { "type": "number" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "fte", "hqCountry", "foundedYear", "description" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" }, "preferences": { "properties": { "avoidInvestors": { "items": { "type": "string" }, "maxItems": 20, "type": "array" }, "geography": { "items": { "type": "string" }, "maxItems": 8, "minItems": 1, "type": "array" }, "preferStrategicValue": { "type": "boolean" }, "requireESGAlignment": { "type": "boolean" } }, "required": [ "geography" ], "type": "object" }, "round": { "properties": { "leadTicketSizeEur": { "minimum": 100000, "type": "number" }, "minViableAmountEur": { "minimum": 100000, "type": "number" }, "stage": { "enum": [ "pre-seed", "seed", "series-a", "series-b", "series-c", "series-d", "series-d-plus", "series-e", "follow-on", "growth", "private-equity" ], "type": "string" }, "targetAmountEur": { "minimum": 100000, "type": "number" }, "targetCloseDate": { "type": "string" }, "valuationTargetEur": { "minimum": 0, "type": "number" } }, "required": [ "stage", "targetAmountEur", "minViableAmountEur", "targetCloseDate", "leadTicketSizeEur" ], "type": "object" } }, "required": [ "company", "round", "preferences" ], "type": "object" }, "name": "investor_shortlist", "outputSchema": null }, { "description": "For CHRO use: analyzes employment contract text to identify and extract IP-related clauses such as invention assignment, confidentiality, non-compete, and patent rights. Returns structured data with clause types, risk levels, and relevant legal context. Ideal for contract review workflows, compliance checks, and IP protection strategy. Sources: USPTO PatFT and EPO Espacenet public datasets. Keywords: employment contract, IP clause, invention assignment, confidentiality agreement, non-compete, patent rights, CHRO tool.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "contractText": { "description": "Full text of the employment contract to analyze", "type": "string" }, "includeContext": { "default": false, "description": "Whether to include legal context for each clause", "type": "boolean" }, "jurisdiction": { "description": "Country/state jurisdiction for legal context (e.g., 'US-CA', 'DE')", "type": "string" } }, "required": [ "contractText" ], "type": "object" }, "name": "ip_contract_clause_extractor", "outputSchema": { "properties": { "clauses": { "items": { "properties": { "context": { "type": "string" }, "endIndex": { "type": "number" }, "riskLevel": { "enum": [ "low", "medium", "high" ], "type": "string" }, "startIndex": { "type": "number" }, "text": { "type": "string" }, "type": { "enum": [ "invention_assignment", "confidentiality", "non_compete", "patent_rights", "other_ip" ], "type": "string" } }, "required": [ "type", "text", "startIndex", "endIndex", "riskLevel" ], "type": "object" }, "type": "array" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "summary": { "properties": { "highRiskCount": { "type": "number" }, "jurisdiction": { "type": "string" }, "totalClauses": { "type": "number" } }, "required": [ "totalClauses", "highRiskCount" ], "type": "object" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status", "clauses", "summary", "warnings" ], "type": "object" } }, { "description": "For CHROs: tracks employee patent filings and flags unassigned inventions. Input employee name or ID to retrieve their patent applications from USPTO and WIPO databases. Returns list of inventions with assignment status, filing dates, and potential ownership gaps. Useful for IP audits, inventor onboarding, and compliance checks. Keywords: patents, IP ownership, employee inventions, USPTO, WIPO.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "companyName": { "description": "Exact legal name of company for assignment check", "type": "string" }, "employeeId": { "description": "Internal employee ID (optional if name provided)", "type": "string" }, "employeeName": { "description": "Full name of employee to track (e.g., 'John Doe')", "type": "string" }, "endDate": { "description": "Filter patents filed before this date (YYYY-MM-DD)", "format": "date", "type": "string" }, "startDate": { "description": "Filter patents filed after this date (YYYY-MM-DD)", "format": "date", "type": "string" } }, "required": [ "employeeName", "companyName" ], "type": "object" }, "name": "ip_employee_invention_tracker", "outputSchema": { "properties": { "companyName": { "type": "string" }, "employeeId": { "type": "string" }, "employeeName": { "type": "string" }, "patents": { "items": { "properties": { "assignee": { "type": "string" }, "filingDate": { "format": "date", "type": "string" }, "inventors": { "items": { "type": "string" }, "type": "array" }, "isAssignedToCompany": { "type": "boolean" }, "patentNumber": { "type": "string" }, "publicationDate": { "format": "date", "type": "string" }, "source": { "enum": [ "USPTO", "WIPO" ], "type": "string" }, "title": { "type": "string" } }, "required": [ "patentNumber", "title", "filingDate", "isAssignedToCompany", "source" ], "type": "object" }, "type": "array" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "totalPatents": { "type": "number" }, "unassignedPatents": { "type": "number" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status", "employeeName", "companyName", "totalPatents", "unassignedPatents", "patents", "warnings" ], "type": "object" } }, { "description": "Pilote de protection IP — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Reference case: Carbios SA — Deeptech FR recyclage PET enzymatique · 14 brevets EP/US/FR · 5 concurrents · licensing €2-8M potentiel. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "country": { "maxLength": 80, "minLength": 2, "type": "string" }, "fte": { "minimum": 1, "type": "integer" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "revenueEur": { "minimum": 0, "type": "number" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "country", "revenueEur", "fte" ], "type": "object" }, "competitors": { "items": { "maxLength": 120, "minLength": 2, "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "focus": { "maxLength": 600, "type": "string" }, "patentPortfolioSummary": { "maxLength": 1000, "minLength": 10, "type": "string" }, "targetMarkets": { "items": { "maxLength": 60, "minLength": 2, "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" } }, "required": [ "company", "patentPortfolioSummary", "targetMarkets", "competitors" ], "type": "object" }, "name": "ip_protection_pilot", "outputSchema": null }, { "description": "Detects potential LLM jailbreak attempts by analyzing user input against NIST AI Risk Management Framework adversarial patterns. Designed for persona risk assessment, this tool evaluates text for common jailbreak techniques such as prompt injection, role-playing, or obfuscation. Inputs include the user message and optional context, returning a risk assessment with confidence scores and pattern matches. Ideal for real-time moderation in chat applications or API gateways.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "context": { "description": "Optional conversation context for better pattern matching", "type": "string" }, "message": { "description": "User input text to analyze for jailbreak attempts", "type": "string" }, "threshold": { "default": 0.7, "description": "Confidence threshold for flagging attempts", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "message" ], "type": "object" }, "name": "jailbreak_attempt_detector", "outputSchema": { "properties": { "isJailbreakAttempt": { "description": "Whether the input exceeds the risk threshold", "type": "boolean" }, "patternsMatched": { "description": "List of detected adversarial patterns", "items": { "type": "string" }, "type": "array" }, "riskScore": { "description": "Confidence score of jailbreak attempt", "maximum": 1, "minimum": 0, "type": "number" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Agrégation d'offres d'emploi publiques pour inférer les tendances de recrutement. Trois modes : (1) company_hiring — analyse des postings d'une société : volume, fonctions (engineering/sales/marketing/ops/finance/hr), seniorité, géographie, croissance vs période précédente, signaux stratégiques inférés ; (2) role_market — volume marché global pour un rôle (open positions estimate, top employeurs, compétences demandées, médiane seniorité) ; (3) competitor_hiring_comparison — comparaison multi-sociétés (total postings, growth%, focus areas). Sources : Adzuna (ADZUNA_APP_ID/KEY env), RemoteOK (keyless), Himalayas (keyless), baseline statique 40 top employeurs. Usages : due diligence VC, intelligence compétitive, benchmarks RH, signaux pivots stratégiques. Cache 6h. SLA ≤15s.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "description": "Nom de la société (pour company_hiring ou comme 1er concurrent)", "type": "string" }, "competitors": { "description": "Liste de sociétés à comparer (pour competitor_hiring_comparison, min 2)", "items": { "type": "string" }, "type": "array" }, "location": { "description": "Pays ou ville (ex. 'France', 'United States', 'London')", "type": "string" }, "mode": { "description": "Mode d'analyse : 'company_hiring' | 'role_market' | 'competitor_hiring_comparison'", "enum": [ "company_hiring", "role_market", "competitor_hiring_comparison" ], "type": "string" }, "period_days": { "description": "Fenêtre d'analyse en jours (défaut 30)", "type": "number" }, "role": { "description": "Intitulé de poste à analyser (pour role_market, ex. 'data scientist', 'compliance officer')", "type": "string" } }, "required": [ "mode" ], "type": "object" }, "name": "job_postings_intelligence", "outputSchema": { "properties": { "company_hiring": { "properties": { "by_function": { "type": "object" }, "by_location": { "type": "object" }, "by_seniority": { "type": "object" }, "company": { "type": "string" }, "growth_pct_vs_prior_period": { "type": "number" }, "inferred_hiring_focus": { "items": { "type": "string" }, "type": "array" }, "inferred_signals": { "items": { "type": "object" }, "type": "array" }, "period_days": { "type": "number" }, "total_postings": { "type": "number" } }, "type": "object" }, "competitor_comparison": { "items": { "properties": { "company": { "type": "string" }, "focus_areas": { "items": { "type": "string" }, "type": "array" }, "growth_pct": { "type": "number" }, "total_postings": { "type": "number" } }, "type": "object" }, "type": "array" }, "mode": { "type": "string" }, "quality_score": { "type": "number" }, "role_market": { "properties": { "location": { "type": "string" }, "median_seniority": { "type": "string" }, "open_positions_estimate": { "type": "number" }, "role": { "type": "string" }, "skills_demanded": { "items": { "type": "object" }, "type": "array" }, "top_employers": { "items": { "type": "object" }, "type": "array" } }, "type": "object" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" } }, "required": [ "mode", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Poll the result of any tool called with async:true. Returns status=pending while running, status=completed with the full result once done, status=failed on error, or status=not_found if the job_id is unknown or expired (TTL 24h).", "inputSchema": { "properties": { "job_id": { "description": "The job_id returned by an async tool call", "type": "string" } }, "required": [ "job_id" ], "type": "object" }, "name": "job_result", "outputSchema": { "oneOf": [ { "properties": { "elapsed_seconds": { "type": "number" }, "status": { "enum": [ "pending" ], "type": "string" } }, "required": [ "status", "elapsed_seconds" ], "title": "Pending" }, { "properties": { "result": { "type": "object" }, "status": { "enum": [ "completed" ], "type": "string" }, "took_seconds": { "type": "number" } }, "required": [ "status", "result", "took_seconds" ], "title": "Completed" }, { "properties": { "error": { "type": "string" }, "status": { "enum": [ "failed" ], "type": "string" }, "took_seconds": { "type": "number" } }, "required": [ "status", "error", "took_seconds" ], "title": "Failed" }, { "properties": { "status": { "enum": [ "not_found" ], "type": "string" } }, "required": [ "status" ], "title": "NotFound" } ], "type": "object" } }, { "description": "Query live Kalshi prediction markets (CFTC-regulated US exchange). Returns question, implied probability (0-1, derived from the yes bid/ask mid), volume, open interest, close time and URL. Optional free-text filter on the question.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "includeRaw": { "description": "Include Kalshi's original fields (default false)", "type": "boolean" }, "includeUnpriced": { "description": "Also return markets with no live bid/ask (default false — they carry no information)", "type": "boolean" }, "limit": { "description": "Maximum markets (default 20)", "maximum": 100, "minimum": 1, "type": "integer" }, "query": { "description": "Free-text filter on the market question", "maxLength": 200, "type": "string" }, "status": { "description": "Market status (default open)", "enum": [ "open", "closed", "settled" ], "type": "string" } }, "type": "object" }, "name": "kalshi_markets", "outputSchema": null }, { "description": "Base de connaissance automatique — Gapup agent-payable C-suite expertise (COO). Returns a structured, audited deliverable. Reference case: Klarna — knowledge base auto · Slack+Notion+Drive · 12 articles seed + structure 8 catégories. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "fte": { "minimum": 1, "type": "integer" }, "name": { "type": "string" }, "sector": { "type": "string" } }, "required": [ "name", "sector", "fte" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" }, "sources": { "items": { "properties": { "accessLevel": { "enum": [ "read", "read-write" ], "type": "string" }, "tool": { "type": "string" }, "volumeApprox": { "type": "string" } }, "required": [ "tool", "volumeApprox", "accessLevel" ], "type": "object" }, "maxItems": 8, "minItems": 2, "type": "array" }, "topPainPoints": { "maxLength": 500, "minLength": 20, "type": "string" } }, "required": [ "company", "sources", "topPainPoints" ], "type": "object" }, "name": "knowledge_base_auto", "outputSchema": null }, { "description": "Screening KYC / AML / Sanctions — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Reference case: Q4 2026 onboarding — 8 entités (UBO chain LLC + SPV offshore), sanctions/PEP/adverse media. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "entities": { "items": { "properties": { "entityType": { "enum": [ "LLC", "Ltd", "SA", "SAS", "GmbH", "BV", "LP", "Trust", "SPV", "Foundation", "individual", "other" ], "type": "string" }, "incorporationDate": { "maxLength": 50, "type": "string" }, "jurisdiction": { "maxLength": 100, "type": "string" }, "legalName": { "maxLength": 200, "type": "string" }, "notes": { "maxLength": 300, "type": "string" }, "registrationNumber": { "maxLength": 100, "type": "string" }, "uboNames": { "items": { "maxLength": 150, "type": "string" }, "type": "array" } }, "required": [ "legalName", "entityType", "jurisdiction" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "onboardingPacket": { "properties": { "accountType": { "enum": [ "corporate-banking", "private-banking", "fund-admin", "broker-dealer", "payment-institution", "other" ], "type": "string" }, "jurisdiction": { "maxLength": 100, "type": "string" }, "requestId": { "maxLength": 100, "type": "string" }, "submittedAt": { "maxLength": 50, "type": "string" }, "submittedBy": { "maxLength": 150, "type": "string" } }, "required": [ "accountType", "jurisdiction" ], "type": "object" }, "riskAppetite": { "default": "standard", "enum": [ "conservative", "standard", "liberal" ], "type": "string" }, "screeningScope": { "properties": { "adverseMedia": { "default": true, "type": "boolean" }, "includeRelatedParties": { "default": true, "type": "boolean" }, "pep": { "default": true, "type": "boolean" }, "sanctions": { "default": true, "type": "boolean" } }, "required": [ "sanctions", "pep", "adverseMedia", "includeRelatedParties" ], "type": "object" } }, "required": [ "onboardingPacket", "entities", "screeningScope", "riskAppetite" ], "type": "object" }, "name": "kyc_screener", "outputSchema": null }, { "description": "Async batch variant of kyc_screener. Accepts 1-100 names and returns immediately (<300ms) with a job_id. The screening runs in the background (up to 10 parallel KYC calls). Poll the result with kyc_screener_batch_result(job_id) after the eta_seconds hint. Each entry can specify name, type (person/company/any), and an optional birthdate hint. Use for bulk client onboarding, UBO list screening, or periodic AML refresh batches. Async tool — register a webhook via `webhooks_manage(register, url, [job.completed])` to receive callbacks instead of polling. Faster + lighter.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "names": { "description": "List of entities to screen (1-100). Each entry requires at minimum a name.", "items": { "properties": { "birthdate": { "description": "Optional birthdate (YYYY-MM-DD) for disambiguation", "type": "string" }, "name": { "description": "Full name of person or legal entity", "maxLength": 200, "minLength": 2, "type": "string" }, "type": { "description": "Entity type hint. Defaults to 'any'.", "enum": [ "person", "company", "any" ], "type": "string" } }, "required": [ "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "names" ], "type": "object" }, "name": "kyc_screener_batch", "outputSchema": { "properties": { "batch_size": { "description": "Number of names queued for screening", "type": "number" }, "eta_seconds": { "description": "Estimated seconds until result is ready", "type": "number" }, "job_id": { "description": "Unique job identifier — pass to kyc_screener_batch_result", "type": "string" }, "status": { "enum": [ "queued" ], "type": "string" }, "submitted_at": { "description": "ISO-8601 submission timestamp", "type": "string" } }, "required": [ "job_id", "status", "eta_seconds", "submitted_at", "batch_size" ], "type": "object" } }, { "description": "Poll the result of a kyc_screener_batch job. Returns status=pending while running, status=completed with the full array of KYC results once done, status=failed on error, or status=not_found if the job_id is unknown or expired (TTL 24h). Call this after the eta_seconds hint returned by kyc_screener_batch.", "inputSchema": { "properties": { "job_id": { "description": "The job_id returned by kyc_screener_batch (prefix: kycb_)", "type": "string" } }, "required": [ "job_id" ], "type": "object" }, "name": "kyc_screener_batch_result", "outputSchema": { "oneOf": [ { "properties": { "elapsed_seconds": { "type": "number" }, "eta_seconds": { "type": [ "number", "null" ] }, "status": { "enum": [ "pending" ], "type": "string" } }, "required": [ "status", "elapsed_seconds" ], "title": "Pending" }, { "properties": { "result": { "properties": { "batch_size": { "type": "number" }, "results": { "items": { "properties": { "error": { "type": "string" }, "name": { "type": "string" }, "result": { "type": "object" }, "status": { "enum": [ "ok", "error" ], "type": "string" } }, "required": [ "name", "status" ], "type": "object" }, "type": "array" }, "screened_at": { "type": "string" } }, "required": [ "batch_size", "results", "screened_at" ], "type": "object" }, "status": { "enum": [ "completed" ], "type": "string" }, "took_seconds": { "type": "number" } }, "required": [ "status", "result", "took_seconds" ], "title": "Completed" }, { "properties": { "error": { "type": "string" }, "status": { "enum": [ "failed" ], "type": "string" }, "took_seconds": { "type": "number" } }, "required": [ "status", "error", "took_seconds" ], "title": "Failed" }, { "properties": { "status": { "enum": [ "not_found" ], "type": "string" } }, "required": [ "status" ], "title": "NotFound" } ], "type": "object" } }, { "description": "Provides CHROs with daily alerts on new labor law changes by jurisdiction (state/country). Inputs include jurisdiction (ISO country/state code) and optional date range. Outputs structured legislative updates with summaries, effective dates, and source links. Useful for compliance monitoring, risk assessment, and policy adjustments. Keywords: labor law, compliance, legislation, jurisdiction, CHRO, HR policy.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "jurisdiction": { "description": "ISO 3166-1 alpha-2 country code or ISO 3166-2 state/province code (e.g., 'US-CA', 'FR')", "type": "string" }, "since": { "description": "Optional start date for changes (YYYY-MM-DD). Defaults to 7 days ago.", "format": "date", "type": "string" }, "until": { "description": "Optional end date for changes (YYYY-MM-DD). Defaults to today.", "format": "date", "type": "string" } }, "required": [ "jurisdiction" ], "type": "object" }, "name": "labor_law_alert_geo", "outputSchema": { "properties": { "changes": { "items": { "properties": { "change_type": { "enum": [ "amendment", "new_law", "repeal" ], "type": "string" }, "effective_date": { "format": "date", "type": "string" }, "jurisdiction": { "type": "string" }, "source_url": { "format": "uri", "type": "string" }, "summary": { "type": "string" }, "title": { "type": "string" } }, "required": [ "title", "effective_date", "jurisdiction", "source_url" ], "type": "object" }, "type": "array" }, "last_updated": { "format": "date-time", "type": "string" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "source_name": { "type": "string" }, "url": { "format": "uri", "type": "string" } }, "required": [ "url", "fetched_at", "source_name" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status", "changes", "warnings", "sources" ], "type": "object" } }, { "description": "Architecte formation & développement — Gapup agent-payable C-suite expertise (CHRO). Returns a structured, audited deliverable. Reference case: Pennylane (180 FTE) — Catalogue 8 formations · 3 parcours individuels · ROI €480k · Payback 7 mois. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "budget": { "properties": { "annualEur": { "minimum": 0, "type": "number" }, "perFteEur": { "minimum": 0, "type": "number" } }, "required": [ "annualEur" ], "type": "object" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "learningNeeds": { "items": { "properties": { "currentCoverage": { "enum": [ "none", "partial", "moderate" ], "type": "string" }, "priority": { "enum": [ "critical", "high", "medium" ], "type": "string" }, "skill": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "skill", "priority", "currentCoverage" ], "type": "object" }, "maxItems": 10, "minItems": 3, "type": "array" }, "team": { "properties": { "departments": { "items": { "maxLength": 100, "type": "string" }, "maxItems": 8, "minItems": 1, "type": "array" }, "growthRate": { "maxLength": 50, "minLength": 2, "type": "string" }, "size": { "maximum": 5000, "minimum": 1, "type": "number" } }, "required": [ "size", "departments", "growthRate" ], "type": "object" } }, "required": [ "company", "team", "learningNeeds", "budget" ], "type": "object" }, "name": "ld_architect", "outputSchema": null }, { "description": "Aimants à leads — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Spendesk — Guide trésorerie startup SaaS B2B FR/EU (2024). Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "brand": { "properties": { "cta": { "maxLength": 150, "minLength": 5, "type": "string" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "sector": { "maxLength": 150, "minLength": 3, "type": "string" } }, "required": [ "name", "sector", "cta" ], "type": "object" }, "icp": { "properties": { "companySize": { "maxLength": 100, "minLength": 2, "type": "string" }, "geography": { "maxLength": 100, "minLength": 2, "type": "string" }, "industry": { "maxLength": 150, "minLength": 3, "type": "string" }, "mainPain": { "maxLength": 300, "minLength": 10, "type": "string" }, "persona": { "maxLength": 300, "minLength": 10, "type": "string" } }, "required": [ "persona", "industry", "companySize", "mainPain", "geography" ], "type": "object" }, "leadMagnet": { "properties": { "budget": { "maxLength": 80, "minLength": 2, "type": "string" }, "format": { "enum": [ "ebook", "benchmark", "calculatrice", "checklist", "template", "guide", "rapport-sectoriel", "mini-cours" ], "type": "string" }, "targetDownloads": { "maxLength": 80, "minLength": 2, "type": "string" }, "topic": { "maxLength": 200, "minLength": 5, "type": "string" } }, "required": [ "topic", "format", "targetDownloads", "budget" ], "type": "object" } }, "required": [ "icp", "leadMagnet", "brand" ], "type": "object" }, "name": "lead_magnets", "outputSchema": null }, { "description": "Structured extraction of clauses, obligations and deadlines from legal documents (SaaS contracts, NDAs, employment agreements, loan agreements, leases, M&A deals, IP licences). Complements contract_risk_scanner with granular per-clause output.\n\nICP: legal ops, M&A lawyers, paralegals, contract managers, compliance officers.\n\nCapabilities:\n• Auto-detects document type (7 types) and language (EN/FR/DE/ES/PT)\n• Extracts parties with roles (buyer, seller, licensor, employee, etc.)\n• Splits document into sections and classifies 16+ clause types\n• Per-clause: 20 obligation patterns (EN/FR/DE), 10 deadline patterns, 18 risk detectors\n• Document-level: red flags (liability cap, auto-renewal, IP overreach, etc.), missing clauses per doc type\n• Global deadline calendar with P0/P1/P2 severity\n• Cross-reference map between sections\n• Cache: 7 days (legal docs stable once provided)\n\n100% pure compute — no external fetch required. Accepts 10k–100k char documents.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "document_text": { "description": "Full text of the legal document (10k–100k chars typical). Plain text or lightly HTML-formatted. EN/FR/DE/ES/PT supported.", "maxLength": 500000, "minLength": 100, "type": "string" }, "document_type": { "description": "Optional. Document type hint. Defaults to auto-detection. Use \"auto\" or omit to let the tool detect from content.", "enum": [ "saas_contract", "employment", "nda", "loan_agreement", "lease", "merger_agreement", "licence_ip", "other", "auto" ], "type": "string" }, "lang": { "description": "Optional. Language hint (e.g. 'en', 'fr', 'de'). Defaults to auto-detection.", "type": "string" }, "target_clauses": { "description": "Optional. Filter extraction to specific clause types. E.g. [\"term\", \"termination\", \"liability\", \"ip\", \"confidentiality\", \"governing_law\", \"indemnification\"]. If omitted or empty, all clauses are extracted.", "items": { "type": "string" }, "type": "array" } }, "required": [ "document_text" ], "type": "object" }, "name": "legal_clause_extractor", "outputSchema": { "properties": { "clauses_extracted": { "items": { "properties": { "deadlines": { "items": { "properties": { "date_or_period": { "type": "string" }, "obligation_link": { "type": "string" }, "trigger_event": { "type": "string" } }, "required": [ "date_or_period" ], "type": "object" }, "type": "array" }, "excerpt": { "type": "string" }, "obligations": { "items": { "properties": { "severity": { "enum": [ "P0", "P1", "P2" ], "type": "string" }, "what": { "type": "string" }, "when": { "type": "string" }, "who": { "type": "string" } }, "required": [ "who", "what", "severity" ], "type": "object" }, "type": "array" }, "risks_detected": { "items": { "type": "string" }, "type": "array" }, "title": { "type": "string" }, "type": { "type": "string" } }, "required": [ "type", "excerpt", "obligations", "deadlines", "risks_detected" ], "type": "object" }, "type": "array" }, "cross_references": { "items": { "properties": { "from_section": { "type": "string" }, "to_section": { "type": "string" } }, "required": [ "from_section", "to_section" ], "type": "object" }, "type": "array" }, "document_type_detected": { "type": "string" }, "effective_date": { "type": "string" }, "governing_law": { "type": "string" }, "jurisdiction": { "type": "string" }, "key_deadlines_global": { "items": { "properties": { "date_or_period": { "type": "string" }, "description": { "type": "string" }, "severity": { "enum": [ "P0", "P1", "P2" ], "type": "string" } }, "required": [ "date_or_period", "description", "severity" ], "type": "object" }, "type": "array" }, "lang_detected": { "type": "string" }, "missing_clauses_expected": { "items": { "type": "string" }, "type": "array" }, "parties_detected": { "items": { "properties": { "name": { "type": "string" }, "role": { "type": "string" } }, "required": [ "role", "name" ], "type": "object" }, "type": "array" }, "quality_score": { "type": "number" }, "red_flags": { "items": { "properties": { "category": { "type": "string" }, "message": { "type": "string" }, "severity": { "enum": [ "P0", "P1", "P2" ], "type": "string" } }, "required": [ "severity", "category", "message" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "partial", "unavailable" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "word_count": { "type": "number" } }, "required": [ "document_type_detected", "lang_detected", "word_count", "parties_detected", "clauses_extracted", "key_deadlines_global", "red_flags", "missing_clauses_expected", "cross_references", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Automates LGPD Data Subject Access Requests (DSARs) for legal teams, handling Brazil-specific data retention, erasure, and access workflows. Accepts user identifiers, request type (access/rectification/deletion), and optional scope filters. Returns structured response with compliance status, warnings, and source references to Brazilian LGPD and CNIL decisions.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "requestType": { "description": "Type of LGPD request", "enum": [ "access", "rectification", "deletion" ], "type": "string" }, "scope": { "description": "Optional list of data categories to limit the request", "items": { "type": "string" }, "type": "array" }, "urgency": { "description": "Priority level for processing", "enum": [ "normal", "high" ], "type": "string" }, "userIdentifier": { "description": "CPF, email, or other unique identifier for the data subject", "type": "string" } }, "required": [ "userIdentifier", "requestType" ], "type": "object" }, "name": "lgpd_data_subject_rights_automator", "outputSchema": { "properties": { "complianceStatus": { "enum": [ "compliant", "non_compliant", "pending" ], "type": "string" }, "dataCategories": { "items": { "type": "string" }, "type": "array" }, "erasureDeadline": { "type": "string" }, "retentionPeriodDays": { "type": "number" }, "sources": { "items": { "properties": { "fetched_at": { "type": "string" }, "title": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Forecasts AI skill demand trends for CHROs by analyzing patent filings (USPTO PatFT) and job postings (BLS API). Returns 12-month skill demand projections with confidence scores, helping HR leaders prioritize workforce upskilling. Inputs: target AI skills (e.g., 'machine learning', 'NLP'), geographic focus (US state/country), and forecast horizon. Outputs include skill growth rates, patent filing trends, and job posting volumes. Keywords: AI workforce planning, skill gap analysis, talent strategy, patent trends, labor market data.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "horizon_months": { "default": 12, "description": "Forecast horizon in months (3-24, default 12)", "type": "number" }, "region": { "description": "Geographic focus (US state code or 'US' for national, e.g., 'CA', 'US')", "type": "string" }, "skills": { "description": "List of AI-related skills to forecast (e.g., ['machine learning', 'computer vision'])", "items": { "type": "string" }, "type": "array" } }, "required": [ "skills", "region" ], "type": "object" }, "name": "lnd_ai_skill_forecast", "outputSchema": { "properties": { "forecast": { "items": { "properties": { "confidence": { "type": "number" }, "growth_rate": { "type": "number" }, "job_posting_trend": { "type": "number" }, "patent_trend": { "type": "number" }, "projected_demand": { "type": "number" }, "skill": { "type": "string" } }, "required": [ "skill", "growth_rate", "confidence" ], "type": "object" }, "type": "array" }, "metadata": { "properties": { "generated_at": { "type": "string" }, "horizon_months": { "type": "number" }, "region": { "type": "string" } }, "type": "object" }, "sources": { "items": { "properties": { "fetched_at": { "type": "string" }, "source": { "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Generates a dynamic skill taxonomy for CHROs by cross-referencing patent filings (USPTO), job postings (BLS), and learning & development data (OECD). Inputs include industry codes, job roles, or skill clusters; outputs structured skill hierarchies with demand trends and competency gaps. Essential for workforce transformation, talent pipeline optimization, and future-proofing organizational capabilities. — pass async:true REQUIRED to avoid x402 timeout.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "industry": { "description": "NAICS industry code or sector name (e.g., '541511' for IT services)", "type": "string" }, "jobRole": { "description": "Target job role or occupation (e.g., 'Data Scientist')", "type": "string" }, "skillCluster": { "description": "Optional skill cluster to focus taxonomy (e.g., 'AI/ML')", "type": "string" }, "timeRange": { "description": "Time range for trend analysis", "enum": [ "1y", "3y", "5y" ], "type": "string" } }, "required": [ "industry" ], "type": "object" }, "name": "lnd_skill_taxonomy_builder", "outputSchema": { "properties": { "industryTrends": { "properties": { "decliningSkills": { "items": { "type": "string" }, "type": "array" }, "emergingSkills": { "items": { "type": "string" }, "type": "array" }, "topSkills": { "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "skillTaxonomy": { "items": { "properties": { "category": { "type": "string" }, "demandScore": { "type": "number" }, "growthRate": { "type": "number" }, "jobPostingFrequency": { "type": "number" }, "lndRelevance": { "type": "number" }, "patentReferences": { "type": "number" }, "skill": { "type": "string" } }, "required": [ "skill", "category", "demandScore" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "source": { "enum": [ "USPTO", "BLS", "OECD" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at", "source" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Tracks real-time ESG incidents in logistics networks for COOs, including supply chain disruptions, regulatory violations, and sustainability risks. Inputs: geographic region, incident type (e.g., emissions, labor, deforestation), and time range. Outputs: structured incident data with severity, location, and source verification. Uses CDP open data and UNCTAD STAT for comprehensive coverage. Keywords: ESG, logistics, supply chain, sustainability, compliance, risk management.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "endDate": { "description": "End date for incident search (ISO 8601)", "format": "date", "type": "string" }, "incidentType": { "description": "Type of ESG incident to track", "enum": [ "emissions", "labor", "deforestation", "water", "waste", "regulatory" ], "type": "string" }, "region": { "description": "Geographic region filter (e.g., 'Europe', 'Asia', 'Global')", "type": "string" }, "severity": { "description": "Minimum severity level to include", "enum": [ "low", "medium", "high", "critical" ], "type": "string" }, "startDate": { "description": "Start date for incident search (ISO 8601)", "format": "date", "type": "string" } }, "required": [ "region", "incidentType" ], "type": "object" }, "name": "logistics_esg_incident_tracker", "outputSchema": { "properties": { "incidents": { "items": { "properties": { "date": { "format": "date-time", "type": "string" }, "description": { "type": "string" }, "id": { "type": "string" }, "incidentType": { "type": "string" }, "location": { "properties": { "coordinates": { "properties": { "lat": { "type": "number" }, "lng": { "type": "number" } }, "type": "object" }, "country": { "type": "string" }, "region": { "type": "string" } }, "type": "object" }, "severity": { "type": "string" }, "source": { "type": "string" }, "sourceUrl": { "type": "string" }, "title": { "type": "string" }, "verificationStatus": { "enum": [ "unverified", "verified", "disputed" ], "type": "string" } }, "required": [ "id", "title", "incidentType", "severity", "location", "date", "source" ], "type": "object" }, "type": "array" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "summary": { "properties": { "byRegion": { "additionalProperties": { "type": "number" }, "type": "object" }, "bySeverity": { "additionalProperties": { "type": "number" }, "type": "object" }, "byType": { "additionalProperties": { "type": "number" }, "type": "object" }, "totalIncidents": { "type": "number" } }, "type": "object" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "As a CFO, identify cross-border M&A arbitrage opportunities by comparing target company valuations across different jurisdictions. Inputs include target company ticker, primary and secondary jurisdictions, and valuation metrics. Outputs include valuation gaps, FX-adjusted multiples, and jurisdiction-specific premiums/discounts. Uses real-time ECB FX rates, Yahoo Finance market data, and SEC EDGAR filings for public companies. Ideal for quick assessment of potential arbitrage in M&A scenarios.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "primaryJurisdiction": { "description": "Primary jurisdiction for valuation comparison (e.g., 'US')", "type": "string" }, "secondaryJurisdiction": { "description": "Secondary jurisdiction for valuation comparison (e.g., 'DE')", "type": "string" }, "sector": { "description": "Industry sector for peer comparison (e.g., 'Technology')", "type": "string" }, "targetTicker": { "description": "Target company ticker symbol (e.g., 'AAPL')", "type": "string" }, "valuationMetric": { "description": "Valuation multiple to use for comparison", "enum": [ "P/E", "EV/EBITDA", "P/B" ], "type": "string" } }, "required": [ "targetTicker", "primaryJurisdiction" ], "type": "object" }, "name": "ma_arbitrage_hunter", "outputSchema": { "properties": { "fxRate": { "type": "number" }, "jurisdictionPremium": { "type": "number" }, "peerMultiples": { "properties": { "primary": { "type": "number" }, "secondary": { "type": "number" } }, "type": "object" }, "primaryValuation": { "type": "number" }, "secondaryValuation": { "type": "number" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "targetCompany": { "type": "string" }, "valuationGap": { "type": "number" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "M&A Deal Screener — Gapup agent-payable C-suite expertise (CSO). Returns a structured, audited deliverable. Reference case: Salesforce M&A targets — 12 cibles screened · fit score + valuation + integration risk. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "acquirer": { "properties": { "arrEur": { "type": "number" }, "name": { "type": "string" }, "sector": { "type": "string" }, "strategicThesis": { "maxLength": 500, "minLength": 20, "type": "string" } }, "required": [ "name", "sector", "arrEur", "strategicThesis" ], "type": "object" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "criteria": { "properties": { "arrRangeMaxEur": { "type": "number" }, "arrRangeMinEur": { "type": "number" }, "preferredEntryMode": { "enum": [ "acquihire", "tuck-in", "platform", "transformative" ], "type": "string" }, "targetGeo": { "items": { "type": "string" }, "type": "array" } }, "required": [ "targetGeo", "arrRangeMinEur", "arrRangeMaxEur", "preferredEntryMode" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" } }, "required": [ "acquirer", "criteria" ], "type": "object" }, "name": "ma_deal_screener", "outputSchema": null }, { "description": "For CFOs evaluating cross-border M&A deals: analyzes tax efficiency by mapping withholding tax rates, transfer pricing regulations, and permanent establishment risks across specified jurisdictions. Inputs include acquirer/target jurisdictions, deal structure, and transaction value. Outputs jurisdiction-specific tax exposure, efficiency scores, and risk flags. Uses World Bank Tax Rates API, IMF SDR data, and SEC EDGAR filings for corporate tax disclosures. — pass async:true REQUIRED to avoid x402 timeout.", "inputSchema": { "properties": { "acquirer_jurisdiction": { "description": "ISO 3166-1 alpha-3 country code of the acquiring entity", "type": "string" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "deal_structure": { "description": "Type of M&A transaction structure", "enum": [ "asset", "share", "merger", "joint_venture" ], "type": "string" }, "include_transfer_pricing": { "default": true, "description": "Whether to analyze transfer pricing risks", "type": "boolean" }, "target_jurisdiction": { "description": "ISO 3166-1 alpha-3 country code of the target entity", "type": "string" }, "transaction_value": { "description": "Deal value in USD millions", "minimum": 0, "type": "number" } }, "required": [ "acquirer_jurisdiction", "target_jurisdiction" ], "type": "object" }, "name": "ma_tax_efficiency_mapper", "outputSchema": { "properties": { "acquirer_tax_rates": { "properties": { "corporate": { "maximum": 100, "minimum": 0, "type": "number" }, "vat": { "maximum": 100, "minimum": 0, "type": "number" }, "withholding": { "maximum": 100, "minimum": 0, "type": "number" } }, "type": "object" }, "efficiency_score": { "maximum": 100, "minimum": 0, "type": "number" }, "permanent_establishment_risk": { "enum": [ "low", "medium", "high" ], "type": "string" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "source": { "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "target_tax_rates": { "properties": { "corporate": { "maximum": 100, "minimum": 0, "type": "number" }, "vat": { "maximum": 100, "minimum": 0, "type": "number" }, "withholding": { "maximum": 100, "minimum": 0, "type": "number" } }, "type": "object" }, "tax_treaties": { "items": { "properties": { "effective_date": { "format": "date", "type": "string" }, "treaty_partner": { "type": "string" }, "withholding_reduction": { "maximum": 100, "minimum": 0, "type": "number" } }, "type": "object" }, "type": "array" }, "transfer_pricing_risk": { "enum": [ "low", "medium", "high" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "As a COO, quickly identify ESG compliance gaps across manufacturing facilities using EPA TRI emissions data and GRI sustainability standards. Input facility identifiers or geographic regions to receive a prioritized remediation roadmap with risk scores, regulatory violations, and suggested corrective actions. Ideal for sustainability reporting, regulatory risk assessment, and operational improvement planning. Keywords: ESG compliance, manufacturing facilities, EPA TRI, GRI standards, sustainability reporting, regulatory risk.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "facilityIds": { "description": "List of EPA facility identifiers (e.g., TRIFID)", "items": { "type": "string" }, "type": "array" }, "includeGri": { "default": true, "description": "Include GRI standards analysis (default: true)", "type": "boolean" }, "region": { "description": "Geographic region (state, county, or ZIP code) for facility search", "type": "string" }, "year": { "description": "Reporting year (default: current year - 1)", "maximum": 2026, "minimum": 2000, "type": "number" } }, "required": [ "facilityIds" ], "type": "object" }, "name": "manufacturing_esg_compliance_mapper", "outputSchema": { "properties": { "facilities": { "items": { "properties": { "complianceGaps": { "items": { "properties": { "description": { "type": "string" }, "severity": { "enum": [ "low", "medium", "high", "critical" ], "type": "string" }, "standard": { "type": "string" }, "suggestedAction": { "type": "string" }, "violationType": { "type": "string" } }, "required": [ "standard", "violationType", "severity", "description" ], "type": "object" }, "type": "array" }, "emissionsSummary": { "properties": { "airReleases": { "type": "number" }, "landReleases": { "type": "number" }, "totalReleases": { "type": "number" }, "waterReleases": { "type": "number" } }, "type": "object" }, "facilityId": { "type": "string" }, "facilityName": { "type": "string" }, "lastInspectionDate": { "format": "date", "type": "string" }, "location": { "type": "string" }, "riskScore": { "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "facilityId", "riskScore", "complianceGaps" ], "type": "object" }, "type": "array" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "summary": { "properties": { "highRiskCount": { "type": "number" }, "topViolationTypes": { "items": { "type": "string" }, "type": "array" }, "totalEmissions": { "type": "number" }, "totalFacilities": { "type": "number" } }, "type": "object" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status", "facilities", "warnings", "sources" ], "type": "object" } }, { "description": "Generates manufacturing waste heatmaps for COOs using EPA TRI and FAOSTAT data. Input manufacturing site identifiers or geographic regions to analyze waste streams, emissions, and resource inefficiencies. Outputs include waste intensity maps, circular economy opportunity rankings, and cost-saving potential. Ideal for sustainability strategy and operational efficiency improvements. Pass async:true to avoid timeout.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "region": { "description": "Geographic region (country code or sub-national region) for aggregated analysis", "type": "string" }, "site_ids": { "description": "List of manufacturing site identifiers (EPA TRI IDs or FAO facility codes)", "items": { "type": "string" }, "type": "array" }, "waste_types": { "description": "Specific waste types to analyze (e.g., ['metals', 'chemicals', 'energy'])", "items": { "type": "string" }, "type": "array" }, "year": { "description": "Analysis year (2010-2023)", "maximum": 2023, "minimum": 2010, "type": "number" } }, "required": [ "year" ], "type": "object" }, "name": "manufacturing_waste_heatmap", "outputSchema": { "properties": { "benchmark_data": { "properties": { "industry_average": { "type": "number" }, "regional_average": { "type": "number" }, "top_performer": { "type": "number" } }, "type": "object" }, "heatmap_data": { "properties": { "regional_aggregates": { "additionalProperties": { "type": "number" }, "type": "object" }, "sites": { "items": { "properties": { "circular_economy_score": { "type": "number" }, "id": { "type": "string" }, "location": { "type": "string" }, "name": { "type": "string" }, "waste_composition": { "additionalProperties": { "type": "number" }, "type": "object" }, "waste_intensity": { "type": "number" } }, "type": "object" }, "type": "array" } }, "type": "object" }, "opportunities": { "items": { "properties": { "description": { "type": "string" }, "implementation_difficulty": { "enum": [ "low", "medium", "high" ], "type": "string" }, "potential_savings": { "type": "number" }, "type": { "type": "string" } }, "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "dataset": { "type": "string" }, "fetched_at": { "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Marge par deal — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Gapup Hub — 8 deals pipeline · €28k ARR sous-marge détecté · Récupération €4.2k/an · Playbook 4 scénarios. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "deals": { "items": { "properties": { "arrEur": { "minimum": 0, "type": "number" }, "closingDate": { "type": "string" }, "cogsEur": { "minimum": 0, "type": "number" }, "competitors": { "items": { "maxLength": 100, "type": "string" }, "maxItems": 3, "minItems": 0, "type": "array" }, "discountPct": { "maximum": 100, "minimum": 0, "type": "number" }, "id": { "maxLength": 50, "minLength": 1, "type": "string" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "urgency": { "enum": [ "high", "medium", "low" ], "type": "string" } }, "required": [ "id", "name", "arrEur", "discountPct" ], "type": "object" }, "maxItems": 15, "minItems": 2, "type": "array" }, "product": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "targetGrossMarginPct": { "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "name", "targetGrossMarginPct" ], "type": "object" } }, "required": [ "company", "product", "deals" ], "type": "object" }, "name": "margin_doctor", "outputSchema": null }, { "description": "Médecin des Marges — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Reference case: Alan — ARR €60M · marge brute 68% → 79% · €3,2M fuites identifiées · Rule of 40 : 14→38. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "businessModel": { "enum": [ "saas", "marketplace", "services", "hybrid" ], "type": "string" }, "currentArrEur": { "type": "number" }, "name": { "type": "string" }, "revenueLastYearEur": { "type": "number" }, "sector": { "type": "string" }, "teamSize": { "type": "number" } }, "required": [ "name", "sector", "currentArrEur", "revenueLastYearEur", "teamSize", "businessModel" ], "type": "object" }, "costBreakdown": { "items": { "properties": { "amountEur": { "type": "number" }, "category": { "type": "string" }, "controllable": { "type": "boolean" }, "percentOfRevenue": { "type": "number" }, "trend": { "enum": [ "growing", "stable", "declining" ], "type": "string" } }, "required": [ "category", "amountEur", "percentOfRevenue", "trend", "controllable" ], "type": "object" }, "type": "array" }, "incomeStatement": { "properties": { "cogsEur": { "type": "number" }, "ebitdaEur": { "type": "number" }, "ebitdaMarginPct": { "type": "number" }, "generalAndAdminEur": { "type": "number" }, "grossMarginPct": { "type": "number" }, "grossRevenueEur": { "type": "number" }, "researchAndDevEur": { "type": "number" }, "salesAndMarketingEur": { "type": "number" } }, "required": [ "grossRevenueEur", "cogsEur", "grossMarginPct", "researchAndDevEur", "salesAndMarketingEur", "generalAndAdminEur", "ebitdaEur", "ebitdaMarginPct" ], "type": "object" }, "marginTargets": { "properties": { "targetEbitdaMarginPct": { "type": "number" }, "targetGrossMarginPct": { "type": "number" }, "targetTimeframeMonths": { "type": "number" } }, "required": [ "targetGrossMarginPct", "targetEbitdaMarginPct", "targetTimeframeMonths" ], "type": "object" }, "unitEconomics": { "properties": { "avgContractValueEur": { "type": "number" }, "cacEur": { "type": "number" }, "grossMarginPerCustomerEur": { "type": "number" }, "ltvEur": { "type": "number" }, "paybackMonths": { "type": "number" } }, "type": "object" } }, "required": [ "company", "incomeStatement", "costBreakdown", "unitEconomics", "marginTargets" ], "type": "object" }, "name": "margin_doctor_finance", "outputSchema": null }, { "description": "Stratégie d'entrée marché — Gapup agent-payable C-suite expertise (CSO). Returns a structured, audited deliverable. Reference case: OpenAI Inde 2026 — entrée marché 1.4Md utilisateurs · 5 forces Porter + 4 entry modes + 18-month roadmap + risk register. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "fte": { "minimum": 1, "type": "integer" }, "homeMarket": { "maxLength": 80, "minLength": 2, "type": "string" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "productDescription": { "maxLength": 400, "minLength": 20, "type": "string" }, "revenueEur": { "minimum": 0, "type": "number" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "fte", "homeMarket", "productDescription" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" }, "preferences": { "properties": { "excludedModes": { "items": { "enum": [ "export", "licensing", "franchising", "partnership", "joint-venture", "wholly-owned-subsidiary", "acquisition" ], "type": "string" }, "maxItems": 4, "type": "array" }, "preferredModes": { "items": { "enum": [ "export", "licensing", "franchising", "partnership", "joint-venture", "wholly-owned-subsidiary", "acquisition" ], "type": "string" }, "maxItems": 4, "type": "array" }, "riskAppetite": { "enum": [ "conservative", "moderate", "aggressive" ], "type": "string" }, "successCriteria": { "maxLength": 400, "type": "string" } }, "required": [ "riskAppetite" ], "type": "object" }, "targetMarket": { "properties": { "country": { "maxLength": 80, "minLength": 2, "type": "string" }, "investmentBudgetEur": { "minimum": 0, "type": "number" }, "preferredTimeline": { "enum": [ "urgent", "12-months", "18-24-months", "exploratory" ], "type": "string" }, "rationale": { "maxLength": 400, "minLength": 20, "type": "string" } }, "required": [ "country", "rationale", "preferredTimeline", "investmentBudgetEur" ], "type": "object" } }, "required": [ "company", "targetMarket", "preferences" ], "type": "object" }, "name": "market_entry_strategist", "outputSchema": null }, { "description": "Generate a structured, sourced market research brief on any market, sector or industry. Returns a machine-readable note with six sections: an executive overview, a market-size estimate (with assumptions and sources — no invented figures), key players, demand & technology trends, risk factors, and a traceable source list. When to use this tool: an agent needs to assess a new market, validate a business opportunity, prepare a pitch, or benchmark a sector before a strategic decision. Data is assembled live from keyless public sources: Wikipedia (sector context), World Bank (macro GDP/population for market sizing), REST Countries (geo context). Fields that cannot be sourced are marked 'unavailable' rather than estimated. Inputs: topic (required), geo and sector (optional refinements).", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "geo": { "description": "Optional geography to scope the brief (country name, region, or continent — e.g. 'France', 'Southeast Asia')", "maxLength": 100, "type": "string" }, "sector": { "description": "Optional parent sector to disambiguate the topic (e.g. 'healthcare', 'energy', 'software')", "maxLength": 100, "type": "string" }, "topic": { "description": "Market or sector to research (e.g. 'electric vehicle batteries', 'B2B SaaS CRM Europe', 'telemedicine Africa')", "maxLength": 200, "minLength": 2, "type": "string" } }, "required": [ "topic" ], "type": "object" }, "name": "market_research_brief", "outputSchema": { "properties": { "generated_at": { "description": "ISO-8601 timestamp of generation", "type": "string" }, "geo": { "type": [ "string", "null" ] }, "key_players": { "properties": { "note": { "type": "string" }, "players": { "items": { "properties": { "description": { "type": [ "string", "null" ] }, "name": { "type": "string" } }, "required": [ "name" ], "type": "object" }, "type": "array" }, "source": { "type": [ "string", "null" ] } }, "required": [ "note", "players" ], "type": "object" }, "market_size_estimate": { "description": "Market size estimate with hypotheses. All figures sourced or marked unavailable.", "properties": { "geo_context": { "description": "Country/region macro context used in sizing", "properties": { "country": { "type": "string" }, "gdp_source": { "type": "string" }, "gdp_usd": { "type": [ "number", "null" ] }, "gdp_year": { "type": [ "string", "null" ] }, "population": { "type": [ "number", "null" ] }, "population_year": { "type": [ "string", "null" ] }, "region": { "type": [ "string", "null" ] } }, "required": [ "country", "gdp_source" ], "type": [ "object", "null" ] }, "methodology_note": { "description": "How the estimate was derived or why it is unavailable", "type": "string" }, "stated_market_size": { "description": "Any market size figure found in public data (Wikidata P2139)", "properties": { "amount_usd": { "type": [ "number", "null" ] }, "caveat": { "type": "string" }, "source": { "type": "string" }, "year_found": { "type": [ "string", "null" ] } }, "type": [ "object", "null" ] } }, "required": [ "methodology_note" ], "type": "object" }, "overview": { "description": "Executive summary of the market", "properties": { "summary": { "type": "string" }, "wikipedia_title": { "type": [ "string", "null" ] }, "wikipedia_url": { "type": [ "string", "null" ] } }, "required": [ "summary" ], "type": "object" }, "risks": { "properties": { "items": { "items": { "properties": { "risk": { "type": "string" }, "severity": { "enum": [ "high", "medium", "low", "unknown" ], "type": "string" } }, "required": [ "risk", "severity" ], "type": "object" }, "type": "array" }, "note": { "type": "string" } }, "required": [ "note", "items" ], "type": "object" }, "sector": { "type": [ "string", "null" ] }, "sources": { "description": "All sources consulted, with URL and retrieval status", "items": { "properties": { "label": { "type": "string" }, "status": { "enum": [ "retrieved", "unavailable", "partial" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "label", "url", "status" ], "type": "object" }, "type": "array" }, "topic": { "type": "string" }, "trends": { "properties": { "items": { "items": { "properties": { "source": { "type": [ "string", "null" ] }, "trend": { "type": "string" } }, "required": [ "trend" ], "type": "object" }, "type": "array" }, "note": { "type": "string" } }, "required": [ "note", "items" ], "type": "object" } }, "required": [ "topic", "geo", "sector", "generated_at", "overview", "market_size_estimate", "key_players", "trends", "risks", "sources" ], "type": "object" } }, { "description": "Dimensionnement marché TAM/SAM/SOM — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Gapup Hub — TAM/SAM/SOM IA décisionnelle C-suite Europe · TAM €48Md · SOM €280M Year-3. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "approach": { "items": { "enum": [ "top-down", "bottom-up", "value-theory", "hybrid" ], "type": "string" }, "maxItems": 3, "minItems": 1, "type": "array" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "competitorComps": { "items": { "type": "string" }, "maxItems": 5, "minItems": 0, "type": "array" }, "horizon": { "enum": [ "current", "3-year", "5-year" ], "type": "string" }, "product": { "properties": { "category": { "maxLength": 120, "minLength": 2, "type": "string" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "valueProposition": { "maxLength": 400, "minLength": 10, "type": "string" } }, "required": [ "name", "category", "valueProposition" ], "type": "object" }, "target": { "properties": { "customerType": { "enum": [ "b2b", "b2c", "b2b2c", "marketplace" ], "type": "string" }, "geography": { "maxLength": 100, "minLength": 2, "type": "string" }, "segments": { "items": { "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" } }, "required": [ "geography", "segments", "customerType" ], "type": "object" } }, "required": [ "product", "target" ], "type": "object" }, "name": "market_sizing", "outputSchema": null }, { "description": "Dashboard ROI marketing — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Gapup Hub — H1 2026 · 5 canaux · ROI 3.2× · Attribution W-shaped · Budget €60k. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "arpuEur": { "type": "number" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "channelData": { "items": { "properties": { "avgCycledays": { "type": "number" }, "channel": { "enum": [ "paid-search", "paid-social", "seo-organic", "email", "events", "content", "partnerships", "outbound", "product-led", "pr" ], "type": "string" }, "closedWon": { "type": "number" }, "mql": { "type": "number" }, "revenueAttribEur": { "type": "number" }, "spendEur": { "type": "number" }, "sql": { "type": "number" } }, "required": [ "channel", "spendEur", "mql", "sql", "closedWon", "revenueAttribEur", "avgCycledays" ], "type": "object" }, "maxItems": 10, "minItems": 2, "type": "array" }, "companyName": { "type": "string" }, "currentAttributionModel": { "enum": [ "first-touch", "last-touch", "linear", "time-decay", "u-shaped", "w-shaped" ], "type": "string" }, "periodLabel": { "type": "string" }, "targetAttributionModel": { "enum": [ "first-touch", "last-touch", "linear", "time-decay", "u-shaped", "w-shaped" ], "type": "string" }, "totalMarketingBudgetEur": { "type": "number" }, "totalRevenueAttribEur": { "type": "number" } }, "required": [ "companyName", "periodLabel", "totalMarketingBudgetEur", "totalRevenueAttribEur", "channelData", "currentAttributionModel", "targetAttributionModel", "arpuEur" ], "type": "object" }, "name": "marketing_roi_dashboard", "outputSchema": null }, { "description": "Scoring MEDDIC du pipeline — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Gapup Hub — Pipeline 8 deals · €2.1M · MEDDIC score moyen 62/100 · 3 deals at-risk. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 100, "type": "string" }, "sector": { "maxLength": 100, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "deals": { "items": { "properties": { "accountName": { "maxLength": 100, "type": "string" }, "closeTargetDays": { "minimum": 0, "type": "integer" }, "competitorPresent": { "maxLength": 100, "type": "string" }, "dealValueEur": { "minimum": 0, "type": "number" }, "id": { "maxLength": 20, "type": "string" }, "meddic": { "items": { "properties": { "dimension": { "enum": [ "metrics", "economic-buyer", "decision-criteria", "decision-process", "identify-pain", "champion" ], "type": "string" }, "evidence": { "maxLength": 300, "type": "string" }, "gaps": { "items": { "type": "string" }, "maxItems": 5, "type": "array" }, "nextActions": { "items": { "type": "string" }, "maxItems": 4, "type": "array" }, "score": { "maximum": 10, "minimum": 0, "type": "integer" } }, "required": [ "dimension", "score", "evidence", "gaps", "nextActions" ], "type": "object" }, "maxItems": 6, "minItems": 1, "type": "array" }, "notes": { "maxLength": 500, "type": "string" }, "stage": { "enum": [ "prospecting", "discovery", "qualification", "proposal", "negotiation", "closing" ], "type": "string" } }, "required": [ "id", "accountName", "dealValueEur", "stage", "closeTargetDays", "meddic" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "product": { "properties": { "avgDealValueEur": { "minimum": 0, "type": "number" }, "name": { "maxLength": 100, "type": "string" } }, "required": [ "name" ], "type": "object" }, "salesCycle": { "properties": { "avgActualDays": { "minimum": 0, "type": "integer" }, "targetDays": { "minimum": 0, "type": "integer" } }, "required": [ "targetDays" ], "type": "object" }, "targetWinRate": { "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "company", "product", "deals" ], "type": "object" }, "name": "meddic_scoring", "outputSchema": null }, { "description": "Monitors AI model output drift by comparing current model responses against MLCommons safety benchmarks. Designed for risk and compliance personas to detect behavioral deviations that may indicate safety or alignment issues. Accepts model outputs or identifiers and returns structured drift metrics with statistical significance. Sources data from MLCommons public benchmark APIs.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "baselineMetrics": { "properties": { "bias": { "maximum": 1, "minimum": 0, "type": "number" }, "hallucinationRate": { "maximum": 1, "minimum": 0, "type": "number" }, "toxicity": { "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "toxicity", "bias", "hallucinationRate" ], "type": "object" }, "currentOutputs": { "description": "Recent model outputs to analyze for drift", "items": { "type": "string" }, "type": "array" }, "modelIdentifier": { "description": "Unique identifier for the model being monitored", "type": "string" }, "threshold": { "default": 0.1, "description": "Drift threshold for alerting", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "modelIdentifier" ], "type": "object" }, "name": "model_behavior_drift_monitor", "outputSchema": { "properties": { "driftMetrics": { "properties": { "bias": { "maximum": 1, "minimum": 0, "type": "number" }, "hallucinationRate": { "maximum": 1, "minimum": 0, "type": "number" }, "overallDriftScore": { "maximum": 1, "minimum": 0, "type": "number" }, "significantDriftDetected": { "type": "boolean" }, "toxicity": { "maximum": 1, "minimum": 0, "type": "number" } }, "type": "object" }, "sources": { "items": { "properties": { "benchmarkVersion": { "type": "string" }, "fetched_at": { "format": "date-time", "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Verifies AI model safety certifications against MLCommons and IEEE 7000 standards. Designed for risk management personas to assess model compliance with established safety benchmarks. Accepts model identifiers or certification IDs and returns structured verification results with source references.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "certification_id": { "description": "Specific certification ID to verify", "type": "string" }, "model_id": { "description": "Unique identifier for the AI model", "type": "string" }, "standard": { "description": "Safety standard to check against", "enum": [ "MLCommons", "IEEE_7000" ], "type": "string" } }, "required": [ "model_id" ], "type": "object" }, "name": "model_safety_certification_checker", "outputSchema": { "properties": { "compliance": { "type": "boolean" }, "last_verified": { "type": "string" }, "sources": { "items": { "properties": { "certification_status": { "type": "string" }, "details": { "type": "object" }, "fetched_at": { "type": "string" }, "standard": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at", "standard" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Pure-compute Monte Carlo portfolio simulation using Geometric Brownian Motion (GBM). Models a multi-asset portfolio across time with contributions, withdrawals, and annual rebalancing. Returns full probability distribution of terminal wealth, percentile paths, drawdown stats, and Sharpe ratio. Modes: simulate (full Monte Carlo) | glide_path (lifecycle 110-age target-date allocation) | stress_test (4 historical crises: 2008 GFC / 2000 dotcom / 1970s stagflation / 2020 COVID). No external data needed — all computed from asset assumptions. Ticker defaults built-in: SPY/VOO/VTI 7%/15%, QQQ 9%/20%, TLT/BND 3%/6%, GLD 5%/18%, BTC 30%/70%. ICP: asset managers, family offices, retail wealth advisors, robo-advisor agents, retirement planners. 10k simulations × 30 years runs in <3s on V8 JIT.", "inputSchema": { "properties": { "assets": { "description": "Portfolio assets. Weights must sum to 1.0 (auto-normalized if not).", "items": { "properties": { "expected_return_pct": { "description": "Annual expected return in % (e.g. 7 for 7%). Uses ticker defaults if omitted.", "type": "number" }, "ticker": { "description": "Asset ticker or identifier (e.g. SPY, QQQ, TLT, GLD, BTC, BND).", "type": "string" }, "volatility_pct": { "description": "Annual volatility (std dev) in % (e.g. 15 for 15%). Uses ticker defaults if omitted.", "type": "number" }, "weight": { "description": "Portfolio weight (0-1). All weights must sum to 1.", "type": "number" } }, "required": [ "ticker", "weight" ], "type": "object" }, "minItems": 1, "type": "array" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "confidence_intervals": { "description": "Percentiles to compute in the output distribution. Default [5, 25, 50, 75, 95].", "items": { "type": "number" }, "type": "array" }, "contributions_annual_eur": { "description": "Annual contribution in EUR (e.g. 12000 for €1000/month).", "type": "number" }, "horizon_years": { "description": "Investment horizon in years (1-50).", "maximum": 50, "minimum": 1, "type": "number" }, "initial_investment_eur": { "description": "Initial capital in EUR (e.g. 100000 for €100k).", "type": "number" }, "mode": { "description": "simulate = full Monte Carlo GBM | glide_path = lifecycle target-date allocation | stress_test = 4 historical crisis scenarios", "enum": [ "simulate", "glide_path", "stress_test" ], "type": "string" }, "simulations": { "description": "Number of Monte Carlo simulations (1000-100000). Default 10000.", "maximum": 100000, "minimum": 1000, "type": "number" }, "target_value_eur": { "description": "Target terminal portfolio value in EUR. Used to compute probability_target_achieved.", "type": "number" }, "withdrawals_annual_eur": { "description": "Annual withdrawal amount in EUR for decumulation phase (e.g. 50000 for €50k/yr).", "type": "number" } }, "required": [ "mode", "assets", "initial_investment_eur", "horizon_years" ], "type": "object" }, "name": "monte_carlo_portfolio", "outputSchema": null }, { "description": "As a CTO, analyze your team's incident response efficiency by breaking down Mean Time To Recovery (MTTR) into root causes: code defects, infrastructure failures, or process bottlenecks. This tool ingests GitHub issue and pull request data alongside Snyk vulnerability reports to provide a detailed breakdown of MTTR components, helping you identify systemic weaknesses in your incident resolution pipeline. Input your GitHub repository details and time range to receive a structured analysis of MTTR contributors with actionable insights.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "githubToken": { "description": "GitHub personal access token for API access", "type": "string" }, "repo": { "description": "Full GitHub repository name (owner/repo)", "type": "string" }, "since": { "description": "Start date for analysis (ISO 8601)", "format": "date-time", "type": "string" }, "snykToken": { "description": "Snyk API token for vulnerability data (optional)", "type": "string" }, "until": { "description": "End date for analysis (ISO 8601)", "format": "date-time", "type": "string" } }, "required": [ "repo", "since", "until", "githubToken" ], "type": "object" }, "name": "mttr_breakdown_analyzer", "outputSchema": { "properties": { "breakdown": { "properties": { "avgResolutionTime": { "description": "Average resolution time in hours", "type": "number" }, "code": { "description": "Percentage of MTTR attributed to code defects", "type": "number" }, "infrastructure": { "description": "Percentage of MTTR attributed to infrastructure failures", "type": "number" }, "process": { "description": "Percentage of MTTR attributed to process bottlenecks", "type": "number" }, "totalIncidents": { "description": "Total number of incidents analyzed", "type": "integer" } }, "required": [ "code", "infrastructure", "process", "totalIncidents", "avgResolutionTime" ], "type": "object" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "topContributors": { "items": { "properties": { "description": { "type": "string" }, "impact": { "description": "Impact score (0-1)", "type": "number" }, "reference": { "description": "GitHub issue/PR or Snyk vulnerability ID", "type": "string" }, "type": { "enum": [ "code", "infrastructure", "process" ], "type": "string" } }, "required": [ "type", "description", "impact", "reference" ], "type": "object" }, "type": "array" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Generates a visual dependency map of supply chain relationships under the NIS2 Directive, scoring criticality based on regulatory sources like EUR-Lex and CNIL decisions. Designed for legal and compliance teams to identify high-risk third-party dependencies. Inputs include organization identifiers and optional scope filters. Outputs structured dependency data with criticality scores and regulatory references.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "depth": { "description": "Dependency chain depth to analyze", "maximum": 5, "minimum": 1, "type": "number" }, "organizationId": { "description": "Unique identifier for the organization (e.g., VAT number or LEI)", "type": "string" }, "scope": { "description": "Analysis scope: full supply chain or critical dependencies only", "enum": [ "full", "critical_only" ], "type": "string" }, "sector": { "description": "NIS2 sector classification (e.g., 'energy', 'transport')", "type": "string" } }, "required": [ "organizationId" ], "type": "object" }, "name": "nis2_supply_chain_dependency_map", "outputSchema": { "properties": { "dependencies": { "items": { "properties": { "criticalityScore": { "maximum": 100, "minimum": 0, "type": "number" }, "dependencies": { "items": { "type": "string" }, "type": "array" }, "id": { "type": "string" }, "name": { "type": "string" }, "nis2Relevance": { "type": "boolean" }, "regulatoryReferences": { "items": { "properties": { "reference": { "type": "string" }, "source": { "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "type": { "type": "string" } }, "required": [ "id", "name", "criticalityScore", "nis2Relevance" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "reference": { "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "As a CTO, extract anomalous log patterns from public breach reports (e.g., Verizon DBIR) and MITRE ATT&CK techniques to optimize SIEM rules and observability pipelines. Inputs include threat actor groups, MITRE tactics (e.g., 'TA0005'), or log sources (e.g., 'AWS CloudTrail'). Outputs structured patterns with MITRE mappings, prevalence scores, and detection recommendations. Ideal for reducing false positives and improving breach detection coverage. Pass async:true to avoid timeout.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "log_source": { "description": "Log source type (e.g., 'AWS CloudTrail', 'Windows Event Log')", "type": "string" }, "max_results": { "default": 10, "maximum": 50, "minimum": 1, "type": "integer" }, "tactic": { "description": "MITRE ATT&CK tactic ID (e.g., 'TA0005')", "type": "string" }, "technique": { "description": "MITRE ATT&CK technique ID (e.g., 'T1059')", "type": "string" }, "threat_actor": { "description": "Threat actor group name (e.g., 'APT29')", "type": "string" } }, "required": [ "tactic" ], "type": "object" }, "name": "observability_log_pattern_miner", "outputSchema": { "properties": { "metadata": { "properties": { "execution_time_ms": { "type": "integer" }, "total_results": { "type": "integer" } }, "type": "object" }, "patterns": { "items": { "properties": { "detection_recommendation": { "type": "string" }, "log_sources": { "items": { "type": "string" }, "type": "array" }, "mitre_tactic": { "type": "string" }, "mitre_technique": { "type": "string" }, "pattern": { "type": "string" }, "prevalence_score": { "maximum": 1, "minimum": 0, "type": "number" }, "references": { "items": { "type": "string" }, "type": "array" } }, "required": [ "pattern", "mitre_technique", "prevalence_score" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "source_type": { "enum": [ "mitre_attack", "github_advisory" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at", "source_type" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status", "patterns", "warnings", "sources" ], "type": "object" } }, { "description": "As a CTO, quickly identify anomalous cloud metrics (CPU, latency, memory) by comparing your infrastructure against AWS public benchmarks and CVE-linked hardware risks. Input your observed metrics (e.g., CPU utilization, request latency) and receive a risk assessment with potential root causes. Ideal for performance troubleshooting, security hardening, and capacity planning. Keywords: cloud observability, anomaly detection, CVE hardware risks, AWS benchmark comparison.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "instanceType": { "pattern": "^[a-z0-9\\.]+$", "type": "string" }, "metricType": { "enum": [ "cpu", "latency", "memory" ], "type": "string" }, "observedValue": { "type": "number" }, "region": { "pattern": "^[a-z]{2}-[a-z]+-[0-9]$", "type": "string" } }, "required": [ "metricType", "observedValue" ], "type": "object" }, "name": "observability_metric_anomaly_detector", "outputSchema": { "properties": { "anomalyScore": { "type": "number" }, "benchmarkValue": { "type": "number" }, "cveRisks": { "items": { "properties": { "cveId": { "type": "string" }, "description": { "type": "string" }, "severity": { "enum": [ "LOW", "MEDIUM", "HIGH", "CRITICAL" ], "type": "string" } }, "required": [ "cveId", "severity" ], "type": "object" }, "type": "array" }, "deviationPercent": { "type": "number" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Onboarding opérationnel des salariés — Gapup agent-payable C-suite expertise (COO). Returns a structured, audited deliverable. Reference case: Pennylane (FR fintech SaaS, ~250 FTE) — 5 parcours 30/60/90 jours · Engineering / Sales / CS / Design / People Ops. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "culture": { "maxLength": 400, "type": "string" }, "fte": { "minimum": 1, "type": "integer" }, "fundingStage": { "type": "string" }, "location": { "type": "string" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" }, "toolStack": { "items": { "type": "string" }, "maxItems": 30, "type": "array" } }, "required": [ "name", "sector", "fte" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" }, "roles": { "items": { "properties": { "avgSalaryEur": { "maximum": 500000, "minimum": 20000, "type": "number" }, "department": { "maxLength": 80, "minLength": 2, "type": "string" }, "roleId": { "maxLength": 80, "minLength": 2, "type": "string" }, "roleTitle": { "maxLength": 120, "minLength": 2, "type": "string" }, "roleType": { "enum": [ "engineering", "sales", "customer-success", "design", "product", "people-ops", "finance", "marketing", "ops", "other" ], "type": "string" }, "seniorityLevel": { "maxLength": 80, "type": "string" } }, "required": [ "roleId", "roleTitle", "roleType", "department" ], "type": "object" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "company", "roles" ], "type": "object" }, "name": "onboarding_salaries", "outputSchema": null }, { "description": "Dashboards opérationnels — Gapup agent-payable C-suite expertise (COO). Returns a structured, audited deliverable. Reference case: Qonto (5 départements · 12 KPIs) — 4 dashboards live en 3 semaines · time-to-décision -55%. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "departments": { "items": { "maxLength": 60, "type": "string" }, "maxItems": 8, "minItems": 1, "type": "array" }, "kpiRequests": { "items": { "properties": { "currentTrackingMethod": { "enum": [ "manual", "spreadsheet", "partial-tool", "none" ], "type": "string" }, "department": { "maxLength": 60, "minLength": 2, "type": "string" }, "frequency": { "enum": [ "realtime", "daily", "weekly", "monthly" ], "type": "string" }, "name": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "department", "currentTrackingMethod", "frequency" ], "type": "object" }, "maxItems": 12, "minItems": 3, "type": "array" }, "primaryDashboardTool": { "enum": [ "metabase", "tableau", "powerbi", "looker", "grafana", "notion", "other", "none" ], "type": "string" }, "techStack": { "items": { "maxLength": 60, "type": "string" }, "maxItems": 10, "minItems": 0, "type": "array" } }, "required": [ "company", "departments", "kpiRequests", "techStack" ], "type": "object" }, "name": "operational_dashboards", "outputSchema": null }, { "description": "As a CTO, track the update velocity of your project's open-source dependencies to assess their impact on DORA metrics like deployment frequency and lead time. This tool fetches release history and version adoption data from npm registry and libraries.io, providing insights into dependency freshness, update frequency, and potential risks. Input a list of package names and optional version ranges to analyze. Outputs structured dependency velocity metrics and warnings about stale or rapidly changing packages.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "lookbackDays": { "maximum": 365, "minimum": 1, "type": "number" }, "packages": { "items": { "properties": { "name": { "type": "string" }, "version": { "type": "string" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "packages" ], "type": "object" }, "name": "oss_dependency_velocity_tracker", "outputSchema": { "properties": { "metrics": { "properties": { "overallVelocityScore": { "type": "number" }, "packages": { "items": { "properties": { "adoptionRate": { "type": "number" }, "currentVersion": { "type": "string" }, "daysSinceLastRelease": { "type": "number" }, "latestVersion": { "type": "string" }, "name": { "type": "string" }, "releasesLast90Days": { "type": "number" }, "velocityScore": { "type": "number" } }, "required": [ "name", "currentVersion", "latestVersion", "releasesLast90Days", "daysSinceLastRelease" ], "type": "object" }, "type": "array" }, "staleDependenciesCount": { "type": "number" } }, "type": "object" }, "sources": { "items": { "properties": { "fetched_at": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "As a CTO, analyze OSSF Scorecard trends for your top 10-50 dependencies to identify security regressions or deteriorating project health. Input GitHub repository names (owner/repo), get structured trend data including score deltas, check failures, and risk flags. Uses OSSF Scorecard API and GitHub Archive for historical context. Ideal for proactive dependency management and risk assessment.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "lookbackDays": { "default": 30, "description": "Number of days to analyze trends for", "maximum": 90, "minimum": 7, "type": "number" }, "minScoreThreshold": { "default": 5, "description": "Minimum acceptable score to flag as risky", "maximum": 10, "minimum": 0, "type": "number" }, "repositories": { "description": "List of GitHub repositories in owner/repo format", "items": { "pattern": "^[a-zA-Z0-9-]+/[a-zA-Z0-9-_.]+$", "type": "string" }, "maxItems": 50, "minItems": 1, "type": "array" } }, "required": [ "repositories" ], "type": "object" }, "name": "ossf_scorecard_trend_analyzer", "outputSchema": { "properties": { "results": { "items": { "properties": { "checks": { "items": { "properties": { "details": { "type": "string" }, "name": { "type": "string" }, "reason": { "type": "string" }, "score": { "type": "number" } }, "required": [ "name", "score" ], "type": "object" }, "type": "array" }, "currentScore": { "type": "number" }, "lastUpdated": { "format": "date-time", "type": "string" }, "previousScore": { "type": "number" }, "repository": { "type": "string" }, "riskFlags": { "items": { "type": "string" }, "type": "array" }, "scoreDelta": { "type": "number" } }, "required": [ "repository", "currentScore", "scoreDelta", "checks", "riskFlags" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Séquences outbound — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Gapup Hub → CFO + CRO B2B SaaS France — Séquence 6 touches multi-canal · Taux réponse +180%. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "excludedAngles": { "maxLength": 400, "type": "string" }, "icp": { "properties": { "companySize": { "maxLength": 100, "minLength": 2, "type": "string" }, "geography": { "maxLength": 100, "minLength": 2, "type": "string" }, "personas": { "items": { "enum": [ "ceo", "cfo", "cmo", "cto", "vp-sales", "vp-ops", "chro", "founder", "procurement", "other" ], "type": "string" }, "maxItems": 4, "minItems": 1, "type": "array" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "sector", "companySize", "geography", "personas" ], "type": "object" }, "offer": { "properties": { "mainBenefit": { "maxLength": 200, "minLength": 10, "type": "string" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "valueProposition": { "maxLength": 400, "minLength": 20, "type": "string" } }, "required": [ "name", "valueProposition", "mainBenefit" ], "type": "object" }, "targetAccounts": { "items": { "maxLength": 100, "minLength": 1, "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" } }, "required": [ "offer", "icp" ], "type": "object" }, "name": "outbound_sequencer", "outputSchema": null }, { "description": "Optimiseur de publicités payantes — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Spendesk (Google + LinkedIn · €45k/mo) — €9k/mo gaspillés identifiés · ROAS LinkedIn ×2.4. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "audienceDescription": { "maxLength": 300, "minLength": 10, "type": "string" }, "campaigns": { "items": { "properties": { "cpc": { "minimum": 0, "type": "number" }, "currentRoas": { "minimum": 0, "type": "number" }, "monthlyBudgetEur": { "minimum": 0, "type": "number" }, "objective": { "maxLength": 60, "minLength": 3, "type": "string" }, "platform": { "enum": [ "google", "meta", "linkedin", "tiktok", "twitter" ], "type": "string" } }, "required": [ "platform", "monthlyBudgetEur", "objective" ], "type": "object" }, "maxItems": 5, "minItems": 1, "type": "array" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "targetMetric": { "enum": [ "roas", "cpl", "cac", "ctr" ], "type": "string" }, "totalMonthlyBudgetEur": { "minimum": 0, "type": "number" } }, "required": [ "company", "campaigns", "totalMonthlyBudgetEur", "targetMetric", "audienceDescription" ], "type": "object" }, "name": "paid_ads_optimizer", "outputSchema": null }, { "description": "Identify and rank strategic partnership opportunities for a company. Returns 5-12 high-fit partnership targets, each scored on revenue lift, time-to-impact, integration complexity and regulatory risk, with a rationale and a recommended first-step outreach playbook. When to use this tool: the user wants business-development or alliance ideas, or M&A target screening before deeper due diligence. Inputs: the user's own company and the strategic axis to unlock through partnership (e.g. enter a new market via distribution, add AI infrastructure without rebuilding). Delivered by Antoine, the AI CSO of the Gapup portfolio.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "constraints": { "properties": { "budgetCeilingEur": { "minimum": 0, "type": "number" }, "mustNotCompeteWith": { "items": { "type": "string" }, "type": "array" }, "regulatoryHotZones": { "items": { "type": "string" }, "type": "array" }, "timeWindow": { "enum": [ "3mo", "6mo", "12mo", "24mo" ], "type": "string" } }, "type": "object" }, "currentPartnerships": { "description": "Existing alliances to factor in", "items": { "type": "string" }, "maxItems": 20, "type": "array" }, "focus": { "maxLength": 800, "type": "string" }, "selfCompany": { "properties": { "arrEur": { "minimum": 0, "type": "number" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "pitch": { "maxLength": 600, "minLength": 10, "type": "string" }, "stage": { "enum": [ "pre-seed", "seed", "series-a", "series-b", "series-c", "growth", "public" ], "type": "string" }, "url": { "format": "uri", "type": "string" } }, "required": [ "name", "pitch" ], "type": "object" }, "strategicAxis": { "description": "What strategic axis to unlock through partnership (e.g. 'enter US market via distribution', 'leverage AI infra without rebuild')", "maxLength": 800, "minLength": 20, "type": "string" } }, "required": [ "selfCompany", "strategicAxis" ], "type": "object" }, "name": "partnership_synergies", "outputSchema": { "properties": { "executiveSummary": { "description": "Board-ready partnership opportunity overview", "type": "string" }, "kpis": { "description": "3-5 headline KPI bubbles", "items": { "properties": { "label": { "type": "string" }, "trend": { "enum": [ "up", "down", "stable" ], "type": "string" }, "value": { "type": "string" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "partnershipTargets": { "description": "5-12 ranked partnership targets", "items": { "properties": { "integrationComplexity": { "enum": [ "low", "medium", "high" ], "type": "string" }, "outreachPlaybook": { "properties": { "firstStep": { "type": "string" }, "keyContact": { "type": "string" }, "openingAngle": { "type": "string" } }, "required": [ "firstStep", "openingAngle" ], "type": "object" }, "partnerName": { "type": "string" }, "partnerUrl": { "type": "string" }, "partnershipType": { "description": "e.g. distribution, technology, co-marketing, OEM, M&A", "type": "string" }, "rank": { "type": "integer" }, "rationale": { "type": "string" }, "regulatoryRisk": { "enum": [ "low", "medium", "high" ], "type": "string" }, "revenueLiftScore": { "maximum": 10, "minimum": 0, "type": "number" }, "timeToImpact": { "enum": [ "3mo", "6mo", "12mo", "24mo" ], "type": "string" } }, "required": [ "rank", "partnerName", "partnershipType", "revenueLiftScore", "rationale" ], "type": "object" }, "type": "array" }, "recommendations": { "description": "Prioritised next steps", "items": { "properties": { "action": { "type": "string" }, "expectedImpact": { "type": "string" }, "horizon": { "type": "string" } }, "required": [ "action", "horizon" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "excerpt": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url" ], "type": "object" }, "type": "array" } }, "required": [ "executiveSummary", "partnershipTargets" ], "type": "object" } }, { "description": "Search, analyze and map patent landscapes across major jurisdictions (US, EP, WO, CN, JP, KR). Three modes: (1) search — find patents by keywords, company name or inventor name; (2) landscape — aggregate distributions: top assignees, top inventors, CPC class breakdown, filings by year, citation leaders, white-space innovation opportunities; (3) lookup — retrieve a specific patent by number (e.g. US10000000B2, EP3456789A1, WO2023/123456). Primary source: WIPO PatentScope (WO PCT, keyless). Optional sources: USPTO PatentsView (US, env PATENTSVIEW_API_KEY), EPO OPS (EP/WO, env EPO_OPS_CONSUMER_KEY + EPO_OPS_CONSUMER_SECRET), Lens.org (global, env LENS_API_TOKEN). Use cases: freedom-to-operate (FTO) analysis, R&D gap identification, VC due diligence IP audit, competitor patent portfolio mapping, inventor network analysis. SLA: <=24s p95 (parallel fetches, 8s per source). Cache: 24h TTL (patent data stable). Quality score: 30 pts per retrieved source (max 90), +10 if >=10 patents, +10 bonus for landscape mode with non-empty top_assignees.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "date_from": { "description": "ISO date YYYY-MM-DD — earliest filing date", "type": "string" }, "date_to": { "description": "ISO date YYYY-MM-DD — latest filing date", "type": "string" }, "jurisdictions": { "description": "Jurisdictions to include. Default: [\"US\",\"EP\",\"WO\"]", "items": { "enum": [ "US", "EP", "WO", "CN", "JP", "KR" ], "type": "string" }, "type": "array" }, "max_results": { "description": "Max patents to return (5-50). Default: 20", "maximum": 50, "minimum": 5, "type": "number" }, "mode": { "description": "search: keyword/inventor/assignee search; landscape: aggregate distributions; lookup: fetch by patent number. Default: \"search\"", "enum": [ "search", "landscape", "lookup" ], "type": "string" }, "query": { "description": "Keywords, company/inventor name, or patent number (e.g. \"machine learning\", \"Tesla Inc\", \"US10000000B2\")", "maxLength": 500, "minLength": 2, "type": "string" } }, "required": [ "query" ], "type": "object" }, "name": "patent_landscape", "outputSchema": { "properties": { "landscape": { "properties": { "citation_leaders": { "items": { "properties": { "citations": { "type": "number" }, "patent_number": { "type": "string" } }, "required": [ "patent_number", "citations" ], "type": "object" }, "type": "array" }, "cpc_distribution": { "items": { "properties": { "code": { "type": "string" }, "count": { "type": "number" }, "label": { "type": "string" }, "pct": { "type": "number" } }, "required": [ "code", "label", "count", "pct" ], "type": "object" }, "type": "array" }, "filings_by_year": { "items": { "properties": { "count": { "type": "number" }, "year": { "type": "number" } }, "required": [ "year", "count" ], "type": "object" }, "type": "array" }, "top_assignees": { "items": { "properties": { "count": { "type": "number" }, "name": { "type": "string" }, "pct": { "type": "number" } }, "required": [ "name", "count", "pct" ], "type": "object" }, "type": "array" }, "top_inventors": { "items": { "properties": { "count": { "type": "number" }, "name": { "type": "string" } }, "required": [ "name", "count" ], "type": "object" }, "type": "array" }, "white_space_hint": { "type": "string" } }, "required": [ "top_assignees", "top_inventors", "cpc_distribution", "filings_by_year", "citation_leaders" ], "type": "object" }, "mode": { "enum": [ "search", "landscape", "lookup" ], "type": "string" }, "patents": { "items": { "properties": { "abstract": { "type": "string" }, "assignee": { "type": "string" }, "citations_received": { "type": "number" }, "cpc_codes": { "items": { "type": "string" }, "type": "array" }, "family_size": { "type": "number" }, "filing_date": { "type": "string" }, "grant_date": { "type": "string" }, "inventors": { "items": { "type": "string" }, "type": "array" }, "ipc_codes": { "items": { "type": "string" }, "type": "array" }, "patent_number": { "type": "string" }, "source": { "enum": [ "USPTO_PatentsView", "Google_Patents", "EPO_OPS", "Lens" ], "type": "string" }, "status": { "enum": [ "granted", "pending", "expired", "withdrawn" ], "type": "string" }, "title": { "type": "string" }, "url": { "type": "string" } }, "required": [ "patent_number", "title", "assignee", "inventors", "filing_date", "abstract", "url", "source" ], "type": "object" }, "type": "array" }, "quality_score": { "type": "number" }, "query": { "type": "string" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "partial", "unavailable" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" } }, "required": [ "query", "mode", "patents", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Async extended variant of patent_landscape. Supports max_results up to 200 (vs 50 in sync mode) and an optional include_citation_graph flag that enriches each patent with its 2-level citation graph (parent patents that cite this one + child patents cited by this one). Returns immediately (<300ms) with a job_id. Poll the result with patent_landscape_result(job_id) after eta_seconds (~180s). Use for deep R&D white-space analysis, freedom-to-operate (FTO) audits, VC due diligence IP mapping, or large-scale competitor portfolio analysis. Async tool — register a webhook via `webhooks_manage(register, url, [job.completed])` to receive callbacks instead of polling. Faster + lighter.", "inputSchema": { "properties": { "date_from": { "description": "ISO date YYYY-MM-DD — earliest filing date", "type": "string" }, "date_to": { "description": "ISO date YYYY-MM-DD — latest filing date", "type": "string" }, "include_citation_graph": { "description": "If true, enriches each patent with a 2-level citation graph (parents + children). Adds significant processing time — use for deep analysis only. Default: false.", "type": "boolean" }, "jurisdictions": { "description": "Jurisdictions to include. Default: [\"US\",\"EP\",\"WO\"]", "items": { "enum": [ "US", "EP", "WO", "CN", "JP", "KR" ], "type": "string" }, "type": "array" }, "max_results": { "description": "Max patents to return (5-200). Default: 20", "maximum": 200, "minimum": 5, "type": "number" }, "mode": { "description": "search / landscape / lookup. Default: \"search\"", "enum": [ "search", "landscape", "lookup" ], "type": "string" }, "query": { "description": "Keywords, company/inventor name, or patent number (e.g. \"machine learning\", \"Tesla Inc\")", "maxLength": 500, "minLength": 2, "type": "string" } }, "required": [ "query" ], "type": "object" }, "name": "patent_landscape_async", "outputSchema": { "properties": { "eta_seconds": { "type": "number" }, "job_id": { "description": "Unique job identifier — pass to patent_landscape_result", "type": "string" }, "status": { "enum": [ "queued" ], "type": "string" }, "submitted_at": { "type": "string" } }, "required": [ "job_id", "status", "eta_seconds", "submitted_at" ], "type": "object" } }, { "description": "Poll the result of a patent_landscape_async job. Returns status=pending while running, status=completed with the full patent landscape report once done, status=failed on error, or status=not_found if the job_id is unknown or expired (TTL 24h). Call this after the eta_seconds hint returned by patent_landscape_async (~180s).", "inputSchema": { "properties": { "job_id": { "description": "The job_id returned by patent_landscape_async (prefix: patl_)", "type": "string" } }, "required": [ "job_id" ], "type": "object" }, "name": "patent_landscape_result", "outputSchema": { "oneOf": [ { "properties": { "elapsed_seconds": { "type": "number" }, "eta_seconds": { "type": [ "number", "null" ] }, "status": { "enum": [ "pending" ], "type": "string" } }, "required": [ "status", "elapsed_seconds" ], "title": "Pending" }, { "properties": { "result": { "description": "Full patent landscape report — patents array, landscape stats, citation_graph if requested", "type": "object" }, "status": { "enum": [ "completed" ], "type": "string" }, "took_seconds": { "type": "number" } }, "required": [ "status", "result", "took_seconds" ], "title": "Completed" }, { "properties": { "error": { "type": "string" }, "status": { "enum": [ "failed" ], "type": "string" }, "took_seconds": { "type": "number" } }, "required": [ "status", "error", "took_seconds" ], "title": "Failed" }, { "properties": { "status": { "enum": [ "not_found" ], "type": "string" } }, "required": [ "status" ], "title": "NotFound" } ], "type": "object" } }, { "description": "Audits patent ownership for employees or contractors, identifying gaps where inventors may not have properly assigned patent rights to the company. Designed for CHROs to ensure IP compliance and mitigate legal risks. Inputs: employee/contractor names or IDs, optional date range. Outputs: list of patents, ownership status, flagged gaps, and assignment details. Sources: USPTO PatFT and EPO Espacenet public records. Keywords: patent audit, IP compliance, employee inventions, contractor agreements, CHRO.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "dateRange": { "description": "Optional date range for patent filings", "properties": { "end": { "format": "date", "type": "string" }, "start": { "format": "date", "type": "string" } }, "type": "object" }, "employeeIds": { "description": "List of employee or contractor IDs (optional if names provided)", "items": { "type": "string" }, "type": "array" }, "employeeNames": { "description": "List of employee or contractor full names to audit", "items": { "type": "string" }, "type": "array" } }, "required": [ "employeeNames" ], "type": "object" }, "name": "patent_ownership_audit", "outputSchema": { "properties": { "gaps": { "items": { "properties": { "employeeName": { "type": "string" }, "issue": { "type": "string" }, "patentId": { "type": "string" } }, "type": "object" }, "type": "array" }, "patents": { "items": { "properties": { "assignee": { "type": "string" }, "filingDate": { "format": "date", "type": "string" }, "inventors": { "items": { "type": "string" }, "type": "array" }, "ownershipStatus": { "enum": [ "assigned", "unassigned", "disputed" ], "type": "string" }, "patentId": { "type": "string" }, "source": { "type": "string" }, "title": { "type": "string" } }, "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "As a CFO, compare cross-border payment rail costs (SWIFT, SEPA, local ACH, stablecoins) with FX conversion fees and settlement times. Input source/destination countries and amount, receive cost breakdown, FX rates, and settlement time estimates. Uses ECB FX rates and World Bank remittance price data for accurate cost analysis. Ideal for optimizing international payment strategies and reducing transaction expenses.", "inputSchema": { "properties": { "amount": { "description": "Transaction amount in source currency", "minimum": 1, "type": "number" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "destination_country": { "description": "ISO 3166-1 alpha-2 country code of payment destination", "type": "string" }, "destination_currency": { "description": "ISO 4217 currency code of destination amount", "type": "string" }, "source_country": { "description": "ISO 3166-1 alpha-2 country code of payment origin", "type": "string" }, "source_currency": { "description": "ISO 4217 currency code of source amount", "type": "string" } }, "required": [ "source_country", "destination_country", "amount" ], "type": "object" }, "name": "payment_rails_cost_analyzer", "outputSchema": { "properties": { "amount": { "type": "number" }, "destination_country": { "type": "string" }, "destination_currency": { "type": "string" }, "fx_rate": { "type": "number" }, "settlement_time": { "properties": { "local_ach": { "type": "string" }, "sepa": { "type": "string" }, "stablecoin": { "type": "string" }, "swift": { "type": "string" } }, "type": "object" }, "source_country": { "type": "string" }, "source_currency": { "type": "string" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "total_cost": { "properties": { "local_ach": { "type": "number" }, "sepa": { "type": "number" }, "stablecoin": { "type": "number" }, "swift": { "type": "number" } }, "type": "object" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Estimateur de scope pentest — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Answers: For a <scope_type> pentest on <tech_stack> with <N> assets, what is the effort and cost estimate? · How much should I budget for a web application + API penetration test for SOC 2 Type II compliance? · What is the standard engagement plan (PTES phases + deliverables) for a <scope_type> pentest? · Which engagement type (black-box/grey-box/white-box/red-team) is recommended for my context? · What are the prerequisites and risks for a pentest engagement on my cloud infrastructure? Reference case: Acme SaaS Inc — Fintech B2B EU · web-app + API REST · 12 microservices Node.js AWS · . Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "asset_count": { "maximum": 500, "minimum": 1, "type": "integer" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "business_context": { "maxLength": 800, "minLength": 10, "type": "string" }, "compliance_frameworks": { "items": { "enum": [ "soc2", "iso27001", "pci-dss", "hipaa", "gdpr" ], "type": "string" }, "maxItems": 5, "type": "array" }, "engagement_type": { "enum": [ "black-box", "grey-box", "white-box", "red-team" ], "type": "string" }, "retest_included": { "type": "boolean" }, "scope_type": { "enum": [ "web-app", "mobile-app", "cloud-infra", "api", "iot", "network-internal", "hybrid" ], "type": "string" }, "target_geos": { "items": { "maxLength": 100, "type": "string" }, "maxItems": 10, "type": "array" }, "tech_stack": { "items": { "maxLength": 200, "type": "string" }, "maxItems": 20, "minItems": 1, "type": "array" } }, "required": [ "scope_type", "tech_stack", "business_context" ], "type": "object" }, "name": "pentest_scope_estimator", "outputSchema": null }, { "description": "Build a complete investor pitch-deck storyline for a company. Returns an 8-20 slide narrative tailored to the target audience (seed-vc / series-a-vc / growth-vc / strategic / bank / grant) — each slide carrying a title, key points, a speaker note and a visual hint — plus a Q&A bank of 10-15 likely board questions and traps to avoid. Output is deck JSON ready to export to Google Slides, Notion or Pitch.com. When to use this tool: the user is preparing a fundraise, a board meeting, or an investor presentation. Inputs: the company profile and the target audience type. Delivered by Sarah, the AI Fundraising lead of the Gapup portfolio.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "audience": { "description": "Target audience — adapts tone + emphasis + Q&A bank", "enum": [ "seed-vc", "series-a-vc", "growth-vc", "strategic", "bank", "grant" ], "type": "string" }, "company": { "properties": { "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "pitch": { "maxLength": 800, "minLength": 20, "type": "string" }, "stage": { "enum": [ "pre-seed", "seed", "series-a", "series-b", "series-c", "growth" ], "type": "string" } }, "required": [ "name", "pitch", "stage" ], "type": "object" }, "keyFacts": { "description": "Hard facts to weave into the deck (traction numbers, milestones, awards)", "items": { "maxLength": 200, "minLength": 8, "type": "string" }, "maxItems": 8, "minItems": 2, "type": "array" }, "slideCount": { "description": "12 = standard VC deck, 15 = bank-friendly with annexes, 20 = growth/strategic", "maximum": 20, "minimum": 8, "type": "integer" } }, "required": [ "company", "audience", "slideCount", "keyFacts" ], "type": "object" }, "name": "pitch_deck_storyline", "outputSchema": { "properties": { "executiveSummary": { "description": "One-paragraph elevator pitch distilled from the deck", "type": "string" }, "kpis": { "description": "3-5 headline KPI bubbles surfaced from keyFacts", "items": { "properties": { "label": { "type": "string" }, "trend": { "enum": [ "up", "down", "stable" ], "type": "string" }, "value": { "type": "string" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "qaBanks": { "description": "10-15 anticipated investor questions with recommended answers", "items": { "properties": { "question": { "type": "string" }, "suggestedAnswer": { "type": "string" }, "trap": { "description": "Common mistake to avoid when answering", "type": "string" } }, "required": [ "question", "suggestedAnswer" ], "type": "object" }, "type": "array" }, "recommendations": { "description": "Fundraising preparation actions", "items": { "properties": { "action": { "type": "string" }, "rationale": { "type": "string" } }, "required": [ "action", "rationale" ], "type": "object" }, "type": "array" }, "slides": { "description": "8-20 slide objects ready to export to Google Slides / Notion / Pitch.com", "items": { "properties": { "keyPoints": { "items": { "type": "string" }, "type": "array" }, "layout": { "description": "Suggested slide layout (title-only, two-column, chart, etc.)", "type": "string" }, "slideNumber": { "type": "integer" }, "speakerNote": { "type": "string" }, "title": { "type": "string" }, "visualHint": { "type": "string" } }, "required": [ "slideNumber", "title", "keyPoints", "speakerNote" ], "type": "object" }, "type": "array" } }, "required": [ "executiveSummary", "slides", "qaBanks" ], "type": "object" } }, { "description": "List live Polymarket events, ranked by volume. An event groups several related markets — use it to discover a topic, then polymarket_markets to price it. Returns title, description, start and end dates, and URL.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "includeClosed": { "description": "Include finished events (default false)", "type": "boolean" }, "limit": { "description": "Maximum events (default 20)", "maximum": 100, "minimum": 1, "type": "integer" } }, "type": "object" }, "name": "polymarket_events", "outputSchema": null }, { "description": "Query live Polymarket prediction markets, ranked by volume. Returns question, implied probability (0-1, derived from the outcome price), volume, liquidity, end date and URL. Optional free-text filter on the question.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "includeClosed": { "description": "Include settled markets (default false)", "type": "boolean" }, "includeRaw": { "description": "Include Polymarket's original fields (default false)", "type": "boolean" }, "limit": { "description": "Maximum markets (default 20)", "maximum": 100, "minimum": 1, "type": "integer" }, "query": { "description": "Free-text filter on the market question", "maxLength": 200, "type": "string" } }, "type": "object" }, "name": "polymarket_markets", "outputSchema": null }, { "description": "Stratège de positionnement — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Gapup Hub vs Tableau/Pigment/Looker — Angle de différenciation + 5 piliers messaging + battle plan. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "aspirations": { "maxLength": 500, "type": "string" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 100, "type": "string" }, "sector": { "maxLength": 100, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "competitors": { "items": { "properties": { "name": { "maxLength": 100, "type": "string" }, "positioning": { "maxLength": 300, "type": "string" }, "strongPoints": { "items": { "type": "string" }, "maxItems": 5, "type": "array" }, "targetSegment": { "maxLength": 200, "type": "string" }, "weakPoints": { "items": { "type": "string" }, "maxItems": 5, "type": "array" } }, "required": [ "name", "positioning" ], "type": "object" }, "maxItems": 8, "minItems": 1, "type": "array" }, "currentWeaknesses": { "items": { "maxLength": 300, "type": "string" }, "maxItems": 5, "type": "array" }, "customerPains": { "items": { "maxLength": 300, "type": "string" }, "maxItems": 8, "minItems": 2, "type": "array" }, "market": { "properties": { "geography": { "maxLength": 100, "type": "string" }, "maturity": { "enum": [ "emerging", "growing", "mature", "declining" ], "type": "string" }, "targetSegment": { "maxLength": 300, "type": "string" } }, "required": [ "targetSegment" ], "type": "object" }, "product": { "properties": { "currentTagline": { "maxLength": 200, "type": "string" }, "mainFeatures": { "items": { "maxLength": 200, "type": "string" }, "maxItems": 8, "minItems": 2, "type": "array" }, "name": { "maxLength": 100, "type": "string" } }, "required": [ "name", "mainFeatures" ], "type": "object" } }, "required": [ "company", "product", "market", "competitors", "customerPains" ], "type": "object" }, "name": "positioning_strategist", "outputSchema": null }, { "description": "Search live prediction markets across Polymarket and Kalshi in one call. Returns a single normalised shape for both venues — question, implied probability (0-1), volume, end date, venue and URL — so you never have to reconcile two different price formats. Raw venue fields are preserved.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "includeClosed": { "description": "Include settled markets (default false)", "type": "boolean" }, "includeRaw": { "description": "Include each venue's original fields (default false)", "type": "boolean" }, "limit": { "description": "Maximum markets to return (default 20)", "maximum": 50, "minimum": 1, "type": "integer" }, "query": { "description": "Free-text filter on the market question. Omit to get the most active markets.", "maxLength": 200, "type": "string" }, "venues": { "description": "Which venues to query (default both)", "items": { "enum": [ "polymarket", "kalshi" ], "type": "string" }, "type": "array" } }, "type": "object" }, "name": "prediction_markets_search", "outputSchema": null }, { "description": "Presse & influenceurs — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Agicap (levée Série C €70M) — CP + 12 contacts presse Tier-1 · plan de diffusion 14 jours. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "announcement": { "properties": { "embargo": { "maxLength": 30, "type": "string" }, "topic": { "maxLength": 300, "minLength": 5, "type": "string" }, "type": { "enum": [ "funding", "product-launch", "milestone", "report", "executive-hire", "partnership", "other" ], "type": "string" } }, "required": [ "topic", "type" ], "type": "object" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "budget": { "minimum": 0, "type": "number" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "targetAudience": { "maxLength": 200, "minLength": 5, "type": "string" }, "targetMedia": { "items": { "enum": [ "tech-press", "financial-press", "industry-press", "generalist", "podcasts", "influencers-linkedin", "influencers-instagram" ], "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "company", "announcement", "targetAudience", "targetMedia" ], "type": "object" }, "name": "press_influencer", "outputSchema": null }, { "description": "Pricing en Deal — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Agicap × Groupe Rocher — Deal €38k · stade négociation · contre-offre -30% · 3 scénarios pricing · ROI 12×. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "averageContractValueEur": { "type": "number" }, "avgDiscountPct": { "type": "number" }, "listPriceEur": { "type": "number" }, "name": { "type": "string" }, "pricingModel": { "enum": [ "per-seat", "usage-based", "flat-rate", "tiered", "hybrid" ], "type": "string" }, "productName": { "type": "string" }, "sector": { "type": "string" } }, "required": [ "name", "sector", "productName", "pricingModel", "averageContractValueEur", "listPriceEur", "avgDiscountPct" ], "type": "object" }, "deal": { "properties": { "budgetConstraintMentioned": { "type": "boolean" }, "competitorsMentioned": { "items": { "type": "string" }, "type": "array" }, "contractLengthMonths": { "type": "number" }, "currentStage": { "enum": [ "discovery", "evaluation", "negotiation", "closing" ], "type": "string" }, "dealSizeEur": { "type": "number" }, "decisionMakerTitle": { "type": "string" }, "prospectName": { "type": "string" }, "prospectSector": { "type": "string" }, "prospectSize": { "enum": [ "startup", "sme", "midmarket", "enterprise" ], "type": "string" } }, "required": [ "prospectName", "prospectSector", "prospectSize", "dealSizeEur", "currentStage", "decisionMakerTitle", "budgetConstraintMentioned", "contractLengthMonths" ], "type": "object" }, "negotiationContext": { "properties": { "championStrength": { "enum": [ "weak", "medium", "strong" ], "type": "string" }, "competitivePressure": { "enum": [ "none", "low", "medium", "high" ], "type": "string" }, "counterOfferEur": { "type": "number" }, "firstProposalSentEur": { "type": "number" }, "mainObjections": { "items": { "type": "string" }, "type": "array" }, "urgencySignals": { "items": { "type": "string" }, "type": "array" } }, "required": [ "mainObjections", "championStrength", "competitivePressure" ], "type": "object" }, "redLines": { "properties": { "maximumDiscountPct": { "type": "number" }, "minimumDealEur": { "type": "number" }, "mustHaveTerms": { "items": { "type": "string" }, "type": "array" } }, "required": [ "minimumDealEur", "maximumDiscountPct" ], "type": "object" } }, "required": [ "company", "deal", "negotiationContext", "redLines" ], "type": "object" }, "name": "pricing_in_deal", "outputSchema": null }, { "description": "Stratège de pricing — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Vercel Pricing 2026 — 4 tiers + usage metering · 3 scenarios pricing chiffrés · ARPU +28% target. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "arrEur": { "minimum": 0, "type": "number" }, "currentArpu": { "type": "number" }, "customerCount": { "minimum": 0, "type": "integer" }, "name": { "type": "string" }, "sector": { "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "competitors": { "items": { "properties": { "anchorPriceEur": { "type": "number" }, "name": { "type": "string" }, "notes": { "maxLength": 300, "type": "string" }, "pricingApproach": { "enum": [ "cost-based", "value-based", "competitive-based", "freemium", "usage-based", "tier-based", "hybrid" ], "type": "string" } }, "required": [ "name", "pricingApproach" ], "type": "object" }, "maxItems": 8, "minItems": 2, "type": "array" }, "currentPricing": { "properties": { "approach": { "enum": [ "cost-based", "value-based", "competitive-based", "freemium", "usage-based", "tier-based", "hybrid" ], "type": "string" }, "currentTiers": { "items": { "type": "string" }, "maxItems": 8, "minItems": 1, "type": "array" }, "painPoints": { "maxLength": 500, "minLength": 20, "type": "string" } }, "required": [ "approach", "currentTiers", "painPoints" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" }, "valueProposition": { "maxLength": 500, "minLength": 20, "type": "string" } }, "required": [ "company", "currentPricing", "competitors", "valueProposition" ], "type": "object" }, "name": "pricing_strategist", "outputSchema": null }, { "description": "Audit conformité vie privée — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Reference case: Lemlist SAS — SaaS outreach B2B, transferts UE→US Schrems II, RGPD + CCPA + LGPD + UK GDPR. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "country": { "maxLength": 80, "minLength": 2, "type": "string" }, "dpaNotifications": { "maxLength": 500, "type": "string" }, "fte": { "minimum": 1, "type": "integer" }, "hasDpo": { "type": "boolean" }, "jurisdictions": { "items": { "enum": [ "FR-EU", "UK", "US-CA", "BR", "other" ], "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "revenueEur": { "minimum": 0, "type": "number" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "fte", "revenueEur", "country", "jurisdictions", "hasDpo" ], "type": "object" }, "focus": { "maxLength": 500, "type": "string" }, "presenterScript": { "items": { "properties": { "durationSec": { "maximum": 120, "minimum": 15, "type": "number" }, "keyPointsToAcknowledge": { "items": { "maxLength": 200, "minLength": 5, "type": "string" }, "maxItems": 5, "type": "array" }, "motionHint": { "enum": [ "opening-confidence", "explaining", "pointing-at-chart", "thoughtful-pause", "emphatic-point", "nodding-listening", "hand-gesture-open", "summary-pose", "closing-confidence" ], "type": "string" }, "narration": { "maxLength": 1200, "minLength": 80, "type": "string" }, "slideId": { "maxLength": 60, "minLength": 2, "type": "string" }, "slideTitle": { "maxLength": 120, "minLength": 3, "type": "string" } }, "required": [ "slideId", "slideTitle", "narration", "durationSec" ], "type": "object" }, "maxItems": 15, "minItems": 6, "type": "array" }, "processingActivities": { "items": { "properties": { "dataCategories": { "items": { "enum": [ "identification", "contact", "financieres", "comportementales", "techniques", "sensibles-art9", "judiciaires-art10", "professionnelles", "localisation" ], "type": "string" }, "maxItems": 8, "minItems": 1, "type": "array" }, "dataSubjects": { "maxLength": 200, "type": "string" }, "description": { "maxLength": 500, "type": "string" }, "estimatedVolume": { "maxLength": 100, "type": "string" }, "internationalTransfers": { "type": "boolean" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "subProcessors": { "items": { "type": "string" }, "maxItems": 10, "type": "array" } }, "required": [ "name", "description", "dataCategories", "dataSubjects", "subProcessors", "internationalTransfers" ], "type": "object" }, "maxItems": 30, "minItems": 1, "type": "array" }, "targetFrameworks": { "items": { "enum": [ "RGPD", "UK GDPR", "CCPA-CPRA", "LGPD", "NIST Privacy Framework", "ISO 27701" ], "type": "string" }, "maxItems": 6, "minItems": 1, "type": "array" } }, "required": [ "company", "processingActivities", "targetFrameworks" ], "type": "object" }, "name": "privacy_compliance_audit", "outputSchema": null }, { "description": "Mapping des process opérationnels — Gapup agent-payable C-suite expertise (COO). Returns a structured, audited deliverable. Reference case: Decathlon France — process Retour produit en magasin · 1700 magasins · 200 retours/j/magasin · -30 à -50% temps cible. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "fte": { "minimum": 1, "type": "integer" }, "locations": { "minimum": 1, "type": "integer" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" }, "toolStack": { "items": { "type": "string" }, "maxItems": 30, "type": "array" } }, "required": [ "name", "sector", "fte" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" }, "presenterScript": { "items": { "properties": { "durationSec": { "maximum": 120, "minimum": 15, "type": "number" }, "keyPointsToAcknowledge": { "items": { "maxLength": 200, "minLength": 5, "type": "string" }, "maxItems": 5, "type": "array" }, "motionHint": { "enum": [ "opening-confidence", "explaining", "pointing-at-chart", "thoughtful-pause", "emphatic-point", "nodding-listening", "hand-gesture-open", "summary-pose", "closing-confidence" ], "type": "string" }, "narration": { "maxLength": 1200, "minLength": 80, "type": "string" }, "slideId": { "maxLength": 60, "minLength": 2, "type": "string" }, "slideTitle": { "maxLength": 120, "minLength": 3, "type": "string" } }, "required": [ "slideId", "slideTitle", "narration", "durationSec" ], "type": "object" }, "maxItems": 15, "minItems": 6, "type": "array" }, "processes": { "items": { "properties": { "currentPainPoints": { "maxLength": 500, "minLength": 10, "type": "string" }, "description": { "maxLength": 400, "minLength": 20, "type": "string" }, "estimatedVolumePerDay": { "minimum": 0, "type": "number" }, "processId": { "maxLength": 80, "minLength": 2, "type": "string" }, "processName": { "maxLength": 160, "minLength": 4, "type": "string" } }, "required": [ "processId", "processName", "description" ], "type": "object" }, "maxItems": 8, "minItems": 1, "type": "array" } }, "required": [ "company", "processes" ], "type": "object" }, "name": "process_mapping", "outputSchema": null }, { "description": "Mining des process — Gapup agent-payable C-suite expertise (COO). Returns a structured, audited deliverable. Reference case: Gapup Hub — 4 process · €320k gaspillage identifié · 3 quick wins · 5 automations. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "companyName": { "type": "string" }, "employeeCount": { "type": "number" }, "mainSystems": { "items": { "type": "string" }, "maxItems": 8, "minItems": 1, "type": "array" }, "objectives": { "items": { "type": "string" }, "maxItems": 4, "minItems": 1, "type": "array" }, "revenueLostEstimateEur": { "type": "number" }, "topProcesses": { "items": { "properties": { "currentPain": { "type": "string" }, "department": { "type": "string" }, "estimatedDurationDays": { "type": "number" }, "involvedRoles": { "items": { "type": "string" }, "type": "array" }, "name": { "type": "string" }, "stepCount": { "type": "number" } }, "required": [ "name", "department", "estimatedDurationDays", "stepCount", "involvedRoles", "currentPain" ], "type": "object" }, "maxItems": 6, "minItems": 2, "type": "array" } }, "required": [ "companyName", "employeeCount", "topProcesses", "objectives", "mainSystems" ], "type": "object" }, "name": "process_mining", "outputSchema": null }, { "description": "Aligns procurement OKRs with ESG targets for COOs using GRI standards and EU TED procurement benchmarks. Inputs include procurement objectives and ESG focus areas (e.g., carbon reduction, supplier diversity). Outputs structured alignment scores, gap analysis, and actionable recommendations. Essential for COOs integrating sustainability into procurement strategy. Keywords: procurement, ESG, GRI, EU TED, OKR alignment, sustainability metrics.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "esgFocusAreas": { "items": { "enum": [ "carbon_reduction", "supplier_diversity", "waste_reduction", "ethical_sourcing", "water_conservation" ], "type": "string" }, "type": "array" }, "industrySector": { "enum": [ "manufacturing", "retail", "healthcare", "technology", "energy", "transportation" ], "type": "string" }, "procurementObjectives": { "items": { "properties": { "description": { "type": "string" }, "id": { "type": "string" }, "weight": { "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "id", "description" ], "type": "object" }, "type": "array" } }, "required": [ "procurementObjectives", "esgFocusAreas" ], "type": "object" }, "name": "procurement_okr_esg_aligner", "outputSchema": { "properties": { "alignmentScores": { "items": { "properties": { "esgArea": { "type": "string" }, "gapDescription": { "type": "string" }, "objectiveId": { "type": "string" }, "score": { "maximum": 100, "minimum": 0, "type": "number" } }, "type": "object" }, "type": "array" }, "benchmarkComparison": { "properties": { "euTedBenchmark": { "type": "number" }, "griComplianceLevel": { "enum": [ "full", "partial", "none" ], "type": "string" }, "industryAverage": { "type": "number" } }, "type": "object" }, "overallAlignmentScore": { "maximum": 100, "minimum": 0, "type": "number" }, "recommendations": { "items": { "properties": { "action": { "type": "string" }, "esgImpact": { "type": "string" }, "priority": { "enum": [ "high", "medium", "low" ], "type": "string" } }, "type": "object" }, "type": "array" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Analyzes procurement waste for COOs using Six Sigma DMAIC framework and EU TED tender data. Identifies non-value-added activities, overprocessing, and inefficiencies in procurement workflows. Inputs include procurement category, time period, and organizational unit. Outputs waste classification, cost impact estimates, and process improvement recommendations. — pass async:true REQUIRED to avoid x402 timeout.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "include_ted_data": { "default": true, "type": "boolean" }, "organizational_unit": { "description": "Specific business unit or department (e.g., 'EMEA', 'Global Operations')", "type": "string" }, "procurement_category": { "description": "Specific procurement category to analyze (e.g., 'IT hardware', 'facilities')", "type": "string" }, "six_sigma_tool": { "default": "DMAIC", "enum": [ "DMAIC", "SIPOC", "ValueStreamMapping" ], "type": "string" }, "time_period": { "description": "Time period for analysis (e.g., '2023-01-01/2023-12-31')", "type": "string" } }, "required": [ "procurement_category", "time_period" ], "type": "object" }, "name": "procurement_six_sigma_waste_hunter", "outputSchema": { "properties": { "cost_impact_estimate": { "type": "number" }, "process_improvement_recommendations": { "items": { "properties": { "estimated_savings": { "type": "number" }, "implementation_difficulty": { "enum": [ "low", "medium", "high" ], "type": "string" }, "recommendation": { "type": "string" } }, "type": "object" }, "type": "array" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "ted_data_coverage": { "type": "number" }, "warnings": { "items": { "type": "string" }, "type": "array" }, "waste_classification": { "properties": { "defects": { "type": "number" }, "inventory": { "type": "number" }, "motion": { "type": "number" }, "overprocessing": { "type": "number" }, "overproduction": { "type": "number" }, "transport": { "type": "number" }, "unused_talent": { "type": "number" }, "waiting": { "type": "number" } }, "type": "object" } }, "required": [ "status" ], "type": "object" } }, { "description": "Optimisation des achats / Spend strategy — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Reference case: Tech SaaS €60M ARR — 200 fournisseurs analysés · 20 leviers chiffrés · -€2.4M opex/an target. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "fte": { "minimum": 1, "type": "integer" }, "name": { "type": "string" }, "revenueEur": { "type": "number" }, "sector": { "type": "string" }, "totalAnnualSpendEur": { "type": "number" } }, "required": [ "name", "sector", "fte", "revenueEur", "totalAnnualSpendEur" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" }, "spendCategories": { "items": { "properties": { "annualSpendEur": { "type": "number" }, "category": { "type": "string" }, "description": { "maxLength": 300, "type": "string" }, "supplierCount": { "type": "integer" } }, "required": [ "category", "annualSpendEur", "supplierCount" ], "type": "object" }, "maxItems": 20, "minItems": 5, "type": "array" }, "topSuppliers": { "items": { "properties": { "annualSpendEur": { "type": "number" }, "category": { "type": "string" }, "contractEnd": { "type": "string" }, "name": { "type": "string" }, "notes": { "maxLength": 300, "type": "string" } }, "required": [ "name", "category", "annualSpendEur" ], "type": "object" }, "maxItems": 50, "minItems": 10, "type": "array" } }, "required": [ "company", "spendCategories", "topSuppliers" ], "type": "object" }, "name": "procurement_spend_optim", "outputSchema": null }, { "description": "For ad_revenue_ops persona: calibrates marketing mix models (MMM) by ingesting OpenRTB impression-level data from FreeWheel Marketplace and other programmatic sources. Accepts model parameters, date ranges, and impression IDs as input, returning structured calibration metrics and attribution adjustments. Useful for improving model accuracy with real-time bidding data and validating revenue attribution across programmatic channels.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "confidenceThreshold": { "default": 0.95, "description": "Confidence threshold for calibration metrics", "maximum": 1, "minimum": 0, "type": "number" }, "endDate": { "description": "End date for impression data (ISO 8601)", "format": "date", "type": "string" }, "impressionIds": { "description": "List of OpenRTB impression IDs to include in calibration", "items": { "type": "string" }, "type": "array" }, "modelId": { "description": "Identifier of the MMM model to calibrate", "type": "string" }, "startDate": { "description": "Start date for impression data (ISO 8601)", "format": "date", "type": "string" } }, "required": [ "modelId", "startDate", "endDate" ], "type": "object" }, "name": "programmatic_attribution_calibrator", "outputSchema": { "properties": { "calibrationMetrics": { "properties": { "attributionAdjustment": { "type": "number" }, "confidenceScore": { "type": "number" }, "impressionCoverage": { "type": "number" }, "modelDrift": { "type": "number" } }, "type": "object" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "record_count": { "type": "number" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Evaluates programmatic ad inventory for brand safety risks using IAB Tech Lab's standards and GDPR-compliant tracking methods. Designed for ad revenue operations teams to assess inventory quality before bidding. Inputs include domain, page URL, and optional contextual signals. Outputs a structured brand safety score with risk categorization and compliance warnings.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "categories": { "description": "Optional IAB content categories for contextual analysis", "items": { "type": "string" }, "type": "array" }, "domain": { "description": "Root domain of the inventory (e.g., 'example.com')", "type": "string" }, "gdprConsent": { "description": "GDPR consent string (TCF v2.0)", "type": "string" }, "url": { "description": "Full page URL being evaluated", "type": "string" } }, "required": [ "domain", "url" ], "type": "object" }, "name": "programmatic_brand_safety_auditor", "outputSchema": { "properties": { "flags": { "items": { "properties": { "category": { "type": "string" }, "description": { "type": "string" }, "severity": { "enum": [ "low", "medium", "high" ], "type": "string" } }, "required": [ "category", "description", "severity" ], "type": "object" }, "type": "array" }, "gdprCompliant": { "type": "boolean" }, "riskLevel": { "enum": [ "low", "medium", "high", "unknown" ], "type": "string" }, "score": { "description": "Brand safety score (0-100)", "type": "number" }, "sources": { "items": { "properties": { "fetched_at": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Générateur de propositions commerciales — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Spendesk × Gapup Hub — Proposition 7 sections · ROI 3Y €1.8M · Payback 4 mois. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "dealContext": { "properties": { "budget": { "type": "string" }, "competitors": { "items": { "maxLength": 100, "type": "string" }, "maxItems": 3, "minItems": 0, "type": "array" }, "decisionTimeline": { "type": "string" } }, "type": "object" }, "offer": { "properties": { "mainBenefits": { "items": { "maxLength": 200, "type": "string" }, "maxItems": 6, "minItems": 2, "type": "array" }, "pricing": { "maxLength": 200, "minLength": 2, "type": "string" }, "productName": { "maxLength": 200, "minLength": 2, "type": "string" }, "useCase": { "maxLength": 300, "minLength": 10, "type": "string" } }, "required": [ "productName", "pricing", "mainBenefits", "useCase" ], "type": "object" }, "prospect": { "properties": { "contactName": { "maxLength": 100, "minLength": 2, "type": "string" }, "contactTitle": { "maxLength": 100, "minLength": 2, "type": "string" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "contactName", "contactTitle" ], "type": "object" } }, "required": [ "company", "prospect", "offer" ], "type": "object" }, "name": "proposal_generator", "outputSchema": null }, { "description": "Préparation Q&A investisseurs — Gapup agent-payable C-suite expertise (FUNDRAISING). Returns a structured, audited deliverable. Reference case: Agicap Série C €70M — 30 Q&A stratégiques · 8 questions pièges · Plan de préparation 21 jours. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "currentArrEur": { "minimum": 0, "type": "number" }, "founded": { "maximum": 2030, "minimum": 2000, "type": "number" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" }, "teamSize": { "maximum": 10000, "minimum": 1, "type": "number" } }, "required": [ "name", "sector", "currentArrEur", "teamSize", "founded" ], "type": "object" }, "founderContext": { "properties": { "knownWeaknesses": { "items": { "maxLength": 200, "type": "string" }, "maxItems": 5, "minItems": 0, "type": "array" }, "pitch": { "maxLength": 1000, "minLength": 30, "type": "string" } }, "required": [ "pitch", "knownWeaknesses" ], "type": "object" }, "round": { "properties": { "leadInvestorProfile": { "maxLength": 200, "minLength": 5, "type": "string" }, "stage": { "enum": [ "pre-seed", "seed", "serie-a", "serie-b", "serie-c", "growth" ], "type": "string" }, "targetEur": { "minimum": 0, "type": "number" } }, "required": [ "stage", "targetEur" ], "type": "object" } }, "required": [ "company", "round", "founderContext" ], "type": "object" }, "name": "qa_pre_flight", "outputSchema": null }, { "description": "QBR automatique CSM — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Gapup Hub × Alan — QBR Q1 2026 · Health score 82/100 · Upsell €18k détecté · Renewal low risk. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "challenges": { "items": { "maxLength": 300, "minLength": 5, "type": "string" }, "maxItems": 4, "minItems": 0, "type": "array" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "customer": { "properties": { "contractStartDate": { "type": "string" }, "contractValueEur": { "minimum": 0, "type": "number" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" }, "tier": { "enum": [ "strategic", "enterprise", "mid-market", "smb" ], "type": "string" } }, "required": [ "name", "sector", "tier", "contractValueEur", "contractStartDate" ], "type": "object" }, "metrics": { "properties": { "featureAdoptionPct": { "maximum": 100, "minimum": 0, "type": "number" }, "npsScore": { "maximum": 10, "minimum": 0, "type": "number" }, "renewalDateDays": { "minimum": 0, "type": "number" }, "supportTickets": { "minimum": 0, "type": "number" }, "usageRate": { "maximum": 100, "minimum": 0, "type": "number" } }, "type": "object" }, "nextQuarterGoals": { "items": { "maxLength": 300, "minLength": 5, "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" }, "period": { "properties": { "quarter": { "enum": [ "Q1", "Q2", "Q3", "Q4" ], "type": "string" }, "year": { "maximum": 2030, "minimum": 2020, "type": "number" } }, "required": [ "quarter", "year" ], "type": "object" }, "wins": { "items": { "maxLength": 300, "minLength": 5, "type": "string" }, "maxItems": 6, "minItems": 1, "type": "array" } }, "required": [ "company", "customer", "period", "metrics", "wins", "challenges", "nextQuarterGoals" ], "type": "object" }, "name": "qbr_auto", "outputSchema": null }, { "description": "Screener deal immobilier (EU) — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Answers: Screen this real estate deal: <address>, <deal_type>, asking €<price> — give me cap rate vs market, location score, risk flags, and deal recommendation. · Should I pursue this hotel investment at <address> for €<price> with <N> keys? Run an EU deal screener with DVF comparables and Géorisques risk data. · What is the real estate market valuation for a <deal_type> at <address> based on recent French DVF transactions? · Run a due diligence deal screen on this property: <address>, €<price>, <sqm> sqm — flood risk, cap rate, price vs comparables. · Evaluate this commercial real estate deal for an investment committee: <deal_type> at <address>, €<price>, NOI €<noi>. Reference case: Hôtel boutique 45 keys · 12 rue de la Paix 75002 Paris · €12.5M · €277k/key · comp DVF €250-380k/key · location 92/100 · score 72 · pursue-with-conditions. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "address": { "maxLength": 300, "minLength": 10, "type": "string" }, "asking_price_eur": { "minimum": 0, "type": "number" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "country_iso2": { "default": "FR", "type": "string" }, "current_noi_eur": { "minimum": 0, "type": "number" }, "deal_type": { "enum": [ "hotel", "office", "retail", "residential-multifamily", "mixed-use", "industrial" ], "type": "string" }, "gross_area_sqm": { "minimum": 0, "type": "number" }, "investment_thesis": { "maxLength": 500, "type": "string" }, "units_or_keys": { "minimum": 0, "type": "integer" } }, "required": [ "deal_type", "address", "country_iso2", "asking_price_eur" ], "type": "object" }, "name": "re_deal_screener", "outputSchema": null }, { "description": "Real estate intelligence aggregator with a best-in-class French dataset (DVF — Demandes de Valeurs Foncières — 100% of FR transactions since 2019, public, keyless) plus UK Land Registry Price Paid (all UK transactions 1995+). Four modes: (1) property — full transaction history for a specific address; (2) comparables — median/std price/m² within a radius (default 500m); (3) market — annual price series, YoY change, volume, trend by commune; (4) valuation — two-method estimate (comparables median + hedonic regression if n≥30) with confidence scoring (high/medium/low). All sources are free and require no API key. ICP: PropTech agents, REITs, fund managers, family offices, insurance. SLA: ≤25s p95 (sources fetched in parallel, 8s budget each). Cache: 24h TTL (DVF data is stable). Quality score: 30 pts DVF retrieved, 20 pts geocoding, 20 pts UK LR retrieved, 15 pts if comparables count ≥10, 15 pts if method quality achieved. Status: failed/<60/≥60 → failed/partial/final. No env vars required.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "date_from": { "description": "ISO date YYYY-MM-DD — earliest transaction date", "type": "string" }, "date_to": { "description": "ISO date YYYY-MM-DD — latest transaction date", "type": "string" }, "location": { "description": "Location descriptor. One of: {address, city?, country?} | {lat, lon, radius_m?} | {insee_code} for FR communes.", "properties": { "address": { "description": "Street address (full or partial)", "type": "string" }, "city": { "description": "City name", "type": "string" }, "country": { "description": "ISO country code or full name", "type": "string" }, "insee_code": { "description": "French INSEE commune code (5 digits, e.g. '75104' for Paris 4e)", "type": "string" }, "lat": { "description": "Latitude", "type": "number" }, "lon": { "description": "Longitude", "type": "number" }, "radius_m": { "description": "Search radius in metres for comparables (default 500)", "type": "number" } }, "type": "object" }, "max_results": { "description": "Maximum number of results to return (5–50, default 20)", "maximum": 50, "minimum": 5, "type": "number" }, "mode": { "description": "property: transactions at an address | comparables: sample around a point | market: commune/neighbourhood market stats | valuation: price estimate for a given surface", "enum": [ "property", "comparables", "market", "valuation" ], "type": "string" }, "property_type": { "description": "Filter by property type (default: all)", "enum": [ "apartment", "house", "land", "commercial", "all" ], "type": "string" }, "surface_max": { "description": "Maximum surface in m² (±20% tolerance applied for comparables)", "type": "number" }, "surface_min": { "description": "Minimum surface in m² (±20% tolerance applied for comparables)", "type": "number" } }, "required": [ "mode", "location" ], "type": "object" }, "name": "real_estate_intel", "outputSchema": { "properties": { "comparables": { "description": "mode=comparables — aggregated comp stats", "properties": { "count": { "type": "number" }, "mean": { "type": "number" }, "median_price_per_m2": { "type": "number" }, "samples": { "items": { "type": "object" }, "type": "array" }, "std": { "type": "number" } }, "type": "object" }, "location_resolved": { "properties": { "address": { "type": "string" }, "city": { "type": "string" }, "country": { "type": "string" }, "insee_code": { "type": "string" }, "lat": { "type": "number" }, "lon": { "type": "number" } }, "required": [ "country" ], "type": "object" }, "market": { "description": "mode=market — commune-level market stats", "properties": { "by_property_type": { "type": "object" }, "median_price_per_m2": { "type": "number" }, "price_history": { "items": { "properties": { "count": { "type": "number" }, "median": { "type": "number" }, "year": { "type": "number" } }, "type": "object" }, "type": "array" }, "trend_direction": { "enum": [ "up", "down", "stable", "unknown" ], "type": "string" }, "volume_transactions": { "type": "number" }, "yoy_change_pct": { "type": "number" } }, "type": "object" }, "mode": { "type": "string" }, "property": { "description": "mode=property — transactions at the location", "items": { "properties": { "address": { "type": "string" }, "date": { "type": "string" }, "price_eur": { "type": "number" }, "price_per_m2": { "type": "number" }, "property_type": { "type": "string" }, "rooms": { "type": "number" }, "surface_m2": { "type": "number" } }, "required": [ "date", "price_eur", "surface_m2", "price_per_m2", "property_type", "address" ], "type": "object" }, "type": "array" }, "quality_score": { "type": "number" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "partial", "unavailable" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "valuation": { "description": "mode=valuation — price estimate", "properties": { "comparables_used": { "type": "number" }, "confidence": { "enum": [ "low", "medium", "high" ], "type": "string" }, "estimate_eur": { "type": "number" }, "method": { "enum": [ "comparables", "hedonic", "fallback" ], "type": "string" }, "range_max": { "type": "number" }, "range_min": { "type": "number" } }, "type": "object" } }, "required": [ "mode", "location_resolved", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "High-frequency real-time market data for trading agents, market-making bots and fintech analysts. Returns FX ticks (bid/ask/spread), intraday OHLCV candles, crypto orderbook snapshots (depth 5-50), recent trades with VWAP, and sovereign bond yields. All sources are keyless public REST APIs (Binance, Coinbase, Kraken, OKX, open FX feeds, worldgovernmentbonds.com). Ultra-short cache: 10s for ticks/trades, 60s for orderbook. Use when an agent needs live market data as precise numeric inputs for trading logic, arbitrage detection, or portfolio valuation.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "depth": { "description": "Orderbook depth (levels each side) for crypto_orderbook mode (default: 20)", "enum": [ 5, 10, 20, 50 ], "type": "number" }, "mode": { "description": "Data stream type: fx_tick (latest FX bid/ask/mid/spread), fx_history_intraday (OHLCV candles), crypto_orderbook (order book snapshot), crypto_trades_recent (last 50 trades + VWAP), bond_yields (sovereign yield %)", "enum": [ "fx_tick", "fx_history_intraday", "crypto_orderbook", "crypto_trades_recent", "bond_yields" ], "type": "string" }, "period": { "description": "Candle period for fx_history_intraday mode (default: 5m)", "enum": [ "5m", "15m", "1h", "1d" ], "type": "string" }, "symbol": { "description": "Market symbol. FX: EURUSD, GBPUSD, USDJPY. Crypto: BTCUSDT, ETHUSDT, BTC-USD. Bonds: US10Y, US2Y, DE10Y, FR10Y, UK10Y, JP10Y, IT10Y", "type": "string" } }, "required": [ "mode", "symbol" ], "type": "object" }, "name": "realtime_data_streams", "outputSchema": { "properties": { "bond_yields": { "type": "object" }, "crypto_orderbook": { "type": "object" }, "crypto_trades": { "type": "object" }, "fx_history": { "type": "object" }, "fx_tick": { "type": "object" }, "mode": { "type": "string" }, "quality_score": { "type": "number" }, "sources": { "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "symbol": { "type": "string" } }, "required": [ "mode", "symbol", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Architecte du recrutement — Gapup agent-payable C-suite expertise (CHRO). Returns a structured, audited deliverable. Reference case: Stripe France — 12 postes Q3 2026 · sourcing multi-canaux + employer brand + frameworks d'entretien + parcours candidat · time-to-hire -45%. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "budget": { "properties": { "averageSalaryRangeEur": { "properties": { "max": { "type": "number" }, "min": { "type": "number" } }, "required": [ "min", "max" ], "type": "object" }, "totalRecruitmentBudgetEur": { "minimum": 0, "type": "number" } }, "required": [ "totalRecruitmentBudgetEur", "averageSalaryRangeEur" ], "type": "object" }, "company": { "properties": { "employerBrandStrength": { "enum": [ "unknown", "emerging", "good", "strong" ], "type": "string" }, "fte": { "minimum": 1, "type": "integer" }, "fundingStage": { "maxLength": 80, "type": "string" }, "location": { "maxLength": 120, "minLength": 2, "type": "string" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "fte", "location", "employerBrandStrength" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" }, "preferences": { "properties": { "diversityFocusAreas": { "items": { "type": "string" }, "maxItems": 6, "type": "array" }, "geography": { "items": { "type": "string" }, "maxItems": 8, "minItems": 1, "type": "array" }, "remotePreference": { "enum": [ "full-remote", "hybrid", "office-first", "office-only" ], "type": "string" } }, "required": [ "geography", "remotePreference" ], "type": "object" }, "roles": { "items": { "properties": { "department": { "maxLength": 80, "minLength": 2, "type": "string" }, "keyMissionPoints": { "maxLength": 400, "type": "string" }, "roleId": { "maxLength": 80, "minLength": 2, "type": "string" }, "roleTitle": { "maxLength": 120, "minLength": 2, "type": "string" }, "roleType": { "enum": [ "engineering", "product", "design", "data", "sales", "customer-success", "marketing", "people-ops", "finance", "operations", "legal", "executive", "other" ], "type": "string" }, "seniority": { "enum": [ "junior", "mid", "senior", "staff", "principal", "manager", "director", "vp", "c-level" ], "type": "string" }, "targetStartDate": { "type": "string" }, "teamSizeReporting": { "minimum": 0, "type": "integer" } }, "required": [ "roleId", "roleTitle", "roleType", "seniority", "department", "targetStartDate" ], "type": "object" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "company", "roles", "budget", "preferences" ], "type": "object" }, "name": "recruiting_architect", "outputSchema": null }, { "description": "Optimiseur de renouvellements — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Gapup Hub — Renewals 10 comptes · €89k ARR à 90j · 3 comptes at-risk · Playbook 6 scénarios. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "accounts": { "items": { "properties": { "arrEur": { "minimum": 0, "type": "number" }, "churnSignals": { "items": { "maxLength": 200, "type": "string" }, "maxItems": 5, "minItems": 0, "type": "array" }, "expansionSignals": { "items": { "maxLength": 200, "type": "string" }, "maxItems": 3, "minItems": 0, "type": "array" }, "healthScore": { "maximum": 100, "minimum": 0, "type": "number" }, "id": { "maxLength": 50, "minLength": 1, "type": "string" }, "lastEngagementDays": { "minimum": 0, "type": "number" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "npsScore": { "maximum": 10, "minimum": 0, "type": "number" }, "openIssues": { "minimum": 0, "type": "number" }, "renewalInDays": { "minimum": 0, "type": "number" }, "tier": { "enum": [ "strategic", "enterprise", "mid-market", "smb" ], "type": "string" }, "usageRatePct": { "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "id", "name", "arrEur", "renewalInDays", "tier" ], "type": "object" }, "maxItems": 20, "minItems": 2, "type": "array" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "horizon": { "enum": [ "30d", "60d", "90d", "180d" ], "type": "string" }, "product": { "properties": { "avgContractLengthMonths": { "maximum": 60, "minimum": 1, "type": "number" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" } }, "required": [ "name" ], "type": "object" }, "targetRenewalRatePct": { "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "company", "product", "accounts" ], "type": "object" }, "name": "renewal_optimizer", "outputSchema": null }, { "description": "Scans for arbitrage opportunities between repo rates (ECB) and short-term funding markets (Treasury Direct). Designed for CFOs to identify cost-effective funding strategies. Inputs include optional date ranges and currency filters. Outputs structured arbitrage opportunities with rate differentials and confidence scores.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "currency": { "enum": [ "EUR", "USD" ], "type": "string" }, "endDate": { "format": "date", "type": "string" }, "minDifferential": { "minimum": 0, "type": "number" }, "startDate": { "format": "date", "type": "string" } }, "required": [], "type": "object" }, "name": "repo_rate_arbitrage_scanner", "outputSchema": { "properties": { "opportunities": { "items": { "properties": { "confidence": { "maximum": 1, "minimum": 0, "type": "number" }, "currency": { "type": "string" }, "date": { "format": "date", "type": "string" }, "differential": { "type": "number" }, "fundingRate": { "type": "number" }, "repoRate": { "type": "number" } }, "required": [ "repoRate", "fundingRate", "differential", "currency", "date" ], "type": "object" }, "type": "array" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Moteur de réputation — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: PayShield SaaS — Monitoring réputation Q2 2026. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "brand": { "maxLength": 200, "minLength": 2, "type": "string" }, "channels": { "items": { "enum": [ "twitter", "linkedin", "g2", "trustpilot", "news", "reddit", "glassdoor", "youtube", "instagram", "other" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "historicalCrises": { "items": { "maxLength": 300, "minLength": 5, "type": "string" }, "maxItems": 5, "type": "array" }, "industry": { "maxLength": 150, "minLength": 2, "type": "string" }, "keywords": { "items": { "maxLength": 100, "minLength": 2, "type": "string" }, "maxItems": 20, "minItems": 1, "type": "array" } }, "required": [ "brand", "keywords", "channels", "industry" ], "type": "object" }, "name": "reputation_engine", "outputSchema": null }, { "description": "Synthèse littérature scientifique (PaperQA2) — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Answers: Conduct a literature review on <topic> — what does the evidence show across recent papers? · Evaluate the current hypothesis that <claim> — supporting and contradicting evidence with citations. · Map contradictions in the literature on <topic> — which camps exist, how many papers per side? · What is the state-of-the-art understanding of <phenomenon> as of <year>? · Perform an interdisciplinary synthesis on <topic> — findings from <domain A> and <domain B>. Reference case: Gut-brain axis · Cognitive performance in healthy adults · OpenAlex+SemanticScholar+CORE · Evidence synthesis · DOI-verified citations · Contradictions + gaps mapped. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "evidence_grade_required": { "default": "standard", "enum": [ "strict", "standard", "broad" ], "type": "string" }, "focus_domain": { "default": "all", "enum": [ "biomedical", "physics", "chemistry", "cs-ml", "economics", "climate", "social-sciences", "engineering", "all" ], "type": "string" }, "include_preprints": { "default": true, "type": "boolean" }, "max_papers": { "default": 15, "maximum": 30, "minimum": 5, "type": "integer" }, "research_question": { "maxLength": 500, "minLength": 30, "type": "string" }, "year_range": { "properties": { "from": { "maximum": 2030, "minimum": 1900, "type": "integer" }, "to": { "maximum": 2030, "minimum": 1900, "type": "integer" } }, "required": [ "from", "to" ], "type": "object" } }, "required": [ "research_question", "focus_domain", "max_papers", "evidence_grade_required", "include_preprints" ], "type": "object" }, "name": "research_paper_qa", "outputSchema": null }, { "description": "Provides unified attribution insights for retail media and programmatic campaigns by analyzing MMM signals from FreeWheel Marketplace and Common Crawl. Designed for ad revenue operations teams to bridge cross-channel performance gaps. Accepts campaign IDs, date ranges, and channel filters as input. Returns structured attribution data with source provenance and confidence scores.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "campaignIds": { "description": "List of campaign identifiers to analyze", "items": { "type": "string" }, "type": "array" }, "channels": { "description": "Channels to include in analysis", "items": { "enum": [ "retail_media", "programmatic", "both" ], "type": "string" }, "type": "array" }, "confidenceThreshold": { "default": 0.7, "description": "Minimum confidence score for included signals", "maximum": 1, "minimum": 0, "type": "number" }, "endDate": { "description": "End date for attribution window (YYYY-MM-DD)", "format": "date", "type": "string" }, "startDate": { "description": "Start date for attribution window (YYYY-MM-DD)", "format": "date", "type": "string" } }, "required": [ "campaignIds", "startDate", "endDate" ], "type": "object" }, "name": "retail_media_attribution_bridge", "outputSchema": { "properties": { "attribution": { "properties": { "campaign_id": { "type": "string" }, "channel": { "type": "string" }, "confidence_score": { "type": "number" }, "conversion_rate": { "type": "number" }, "last_updated": { "format": "date-time", "type": "string" } }, "required": [ "campaign_id", "channel", "conversion_rate", "confidence_score" ], "type": "object" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "source_type": { "enum": [ "freewheel", "common_crawl" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at", "source_type" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Audits retail media networks for ESG compliance by analyzing ad placements, tracking cookies, and verifying ethical advertising standards. Designed for ad_revenue_ops teams to ensure GDPR and sustainability compliance across digital retail platforms. Accepts domain lists or network identifiers as input and returns structured compliance reports with warnings and source references. Requires async:true to avoid timeout errors.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "checkESG": { "default": true, "description": "Enable ESG advertising standards compliance check", "type": "boolean" }, "checkGDPR": { "default": true, "description": "Enable GDPR cookie tracking compliance check", "type": "boolean" }, "domains": { "description": "List of retail media network domains to audit", "items": { "type": "string" }, "type": "array" }, "networkIds": { "description": "List of retail media network identifiers", "items": { "type": "string" }, "type": "array" } }, "required": [], "type": "object" }, "name": "retail_media_esg_compliance", "outputSchema": { "properties": { "sources": { "items": { "properties": { "compliance_score": { "type": "number" }, "fetched_at": { "type": "string" }, "url": { "type": "string" }, "violations": { "items": { "properties": { "description": { "type": "string" }, "severity": { "enum": [ "low", "medium", "high" ], "type": "string" }, "type": { "type": "string" } }, "type": "object" }, "type": "array" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "summary": { "properties": { "compliant_networks": { "type": "number" }, "esg_violations": { "type": "number" }, "gdpr_violations": { "type": "number" }, "total_networks": { "type": "number" } }, "type": "object" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Architecte RevOps — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Qonto — ARR €200M · 200 reps · forecast ±35% · fuite €4,2M/an identifiée · plan RevOps 12 semaines. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "currentArrEur": { "type": "number" }, "name": { "type": "string" }, "salesMotion": { "enum": [ "inbound", "outbound", "plg", "hybrid" ], "type": "string" }, "sector": { "type": "string" }, "teamSize": { "type": "number" } }, "required": [ "name", "sector", "currentArrEur", "teamSize", "salesMotion" ], "type": "object" }, "currentPainPoints": { "items": { "type": "string" }, "type": "array" }, "currentStack": { "items": { "properties": { "category": { "enum": [ "crm", "marketing-automation", "sdr-tool", "analytics", "revenue-intelligence", "cs-platform", "other" ], "type": "string" }, "satisfaction": { "enum": [ "poor", "ok", "good" ], "type": "string" }, "tool": { "type": "string" } }, "required": [ "tool", "category", "satisfaction" ], "type": "object" }, "type": "array" }, "horizonMonths": { "type": "number" }, "keyMetrics": { "properties": { "avgSalesCycleDays": { "type": "number" }, "dataQualityScore": { "enum": [ "poor", "fair", "good", "excellent" ], "type": "string" }, "forecastAccuracyPct": { "type": "number" }, "leadToOpportunityPct": { "type": "number" }, "opportunityToClosePct": { "type": "number" } }, "type": "object" }, "objectives": { "items": { "type": "string" }, "type": "array" }, "revenueTeam": { "properties": { "aes": { "type": "number" }, "csms": { "type": "number" }, "hasRevOpsFunction": { "type": "boolean" }, "revOpsHeadcount": { "type": "number" }, "sdrs": { "type": "number" } }, "required": [ "sdrs", "aes", "csms", "hasRevOpsFunction" ], "type": "object" } }, "required": [ "company", "revenueTeam", "currentStack", "currentPainPoints", "keyMetrics", "objectives", "horizonMonths" ], "type": "object" }, "name": "revops_architect", "outputSchema": null }, { "description": "Architecte d'appels d'offres — Gapup agent-payable C-suite expertise (COO). Returns a structured, audited deliverable. Reference case: AO DINUM — Plateforme IA souveraine. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "budgetRange": { "maxLength": 200, "minLength": 3, "type": "string" }, "clientCompany": { "maxLength": 200, "minLength": 2, "type": "string" }, "competitorsLikely": { "items": { "maxLength": 120, "minLength": 2, "type": "string" }, "maxItems": 8, "minItems": 1, "type": "array" }, "compliancePoints": { "items": { "maxLength": 200, "minLength": 2, "type": "string" }, "maxItems": 20, "minItems": 0, "type": "array" }, "deadlineISO": { "maxLength": 30, "minLength": 8, "type": "string" }, "ourPositioning": { "maxLength": 800, "minLength": 20, "type": "string" }, "rfpScope": { "maxLength": 1000, "minLength": 20, "type": "string" }, "rfpType": { "enum": [ "public", "private" ], "type": "string" } }, "required": [ "clientCompany", "rfpType", "rfpScope", "ourPositioning", "competitorsLikely", "budgetRange", "deadlineISO" ], "type": "object" }, "name": "rfp_tender_architect", "outputSchema": null }, { "description": "Architecte de politique RSE — Gapup agent-payable C-suite expertise (SUSTAINABILITY). Returns a structured, audited deliverable. Reference case: TechCorp SAS — Politique RSE 2025-2028 (500 FTE, €60M CA, SaaS B2B France). Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "ambitions": { "maxLength": 800, "minLength": 20, "type": "string" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "country": { "maxLength": 80, "minLength": 2, "type": "string" }, "foundedYear": { "maximum": 2100, "minimum": 1800, "type": "integer" }, "fte": { "minimum": 1, "type": "integer" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "revenueEur": { "minimum": 0, "type": "number" }, "sector": { "maxLength": 160, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "fte", "revenueEur", "country" ], "type": "object" }, "currentInitiatives": { "items": { "maxLength": 200, "minLength": 5, "type": "string" }, "maxItems": 10, "type": "array" }, "focus": { "maxLength": 500, "type": "string" }, "targetLabels": { "items": { "enum": [ "B-Corp", "EcoVadis Bronze", "EcoVadis Silver", "EcoVadis Gold", "EcoVadis Platinum", "Lucie 26000", "ISO 14001", "ISO 26000", "Entreprise à mission" ], "type": "string" }, "type": "array" }, "targetStakeholders": { "items": { "enum": [ "clients", "employés", "investisseurs", "fournisseurs", "communauté", "régulateurs" ], "type": "string" }, "maxItems": 6, "minItems": 1, "type": "array" }, "values": { "items": { "maxLength": 100, "minLength": 3, "type": "string" }, "maxItems": 10, "minItems": 2, "type": "array" } }, "required": [ "company", "values", "ambitions", "targetStakeholders" ], "type": "object" }, "name": "rse_policy_builder", "outputSchema": null }, { "description": "Enables CHROs to benchmark their company's sabbatical policies against peer organizations using data from SHRM, Payscale, and Mercer. Inputs include company size, industry, and current policy details. Outputs structured comparison with cost impact analysis, eligibility criteria, and duration benchmarks. Ideal for strategic HR planning and policy optimization.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "companySize": { "description": "Number of employees in the company", "type": "number" }, "currentPolicy": { "properties": { "durationWeeks": { "description": "Current sabbatical duration in weeks", "type": "number" }, "eligibilityYears": { "description": "Years of service required for eligibility", "type": "number" }, "payPercentage": { "description": "Percentage of pay during sabbatical", "type": "number" } }, "required": [ "durationWeeks", "eligibilityYears", "payPercentage" ], "type": "object" }, "industry": { "description": "Industry classification code (NAICS)", "type": "string" }, "peerGroup": { "description": "List of peer company names for direct comparison", "items": { "type": "string" }, "type": "array" } }, "required": [ "companySize", "industry", "currentPolicy" ], "type": "object" }, "name": "sabbatical_policy_comparator", "outputSchema": { "properties": { "benchmark": { "properties": { "industryAverage": { "properties": { "costImpact": { "type": "number" }, "durationWeeks": { "type": "number" }, "eligibilityYears": { "type": "number" }, "payPercentage": { "type": "number" } }, "type": "object" }, "peerComparison": { "items": { "properties": { "company": { "type": "string" }, "costImpact": { "type": "number" }, "durationWeeks": { "type": "number" }, "eligibilityYears": { "type": "number" }, "payPercentage": { "type": "number" } }, "type": "object" }, "type": "array" }, "recommendations": { "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "sources": { "items": { "properties": { "fetched_at": { "type": "string" }, "source": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at", "source" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Analyzes potential LLM guardrail breaches against IEEE 7000 ethical compliance standards. Designed for risk persona to evaluate safety violations in AI outputs. Accepts raw LLM responses or structured breach reports, returns compliance analysis with severity scoring and mitigation recommendations.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "context": { "description": "Contextual information about the prompt or conversation", "type": "string" }, "includeMitigations": { "default": true, "description": "Whether to include mitigation recommendations", "type": "boolean" }, "llmOutput": { "description": "Raw text output from LLM to analyze for guardrail breaches", "type": "string" }, "severityThreshold": { "default": 5, "description": "Minimum severity score to report (0-10 scale)", "maximum": 10, "minimum": 0, "type": "number" } }, "required": [ "llmOutput" ], "type": "object" }, "name": "safety_guardrail_breach_analyzer", "outputSchema": { "properties": { "breaches": { "items": { "properties": { "evidence": { "type": "string" }, "mitigation": { "type": "string" }, "principle": { "type": "string" }, "severity": { "maximum": 10, "minimum": 0, "type": "number" }, "violation": { "type": "string" } }, "required": [ "principle", "violation", "severity", "evidence" ], "type": "object" }, "type": "array" }, "complianceScore": { "maximum": 100, "minimum": 0, "type": "number" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Logs AI safety violations for compliance reporting, targeting risk management personas. Accepts incident details such as violation type, severity, description, and timestamp. Returns structured data with compliance categorization based on NIST AI RMF guidelines. Ideal for automated incident tracking and regulatory reporting workflows.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "description": { "maxLength": 2000, "minLength": 10, "type": "string" }, "metadata": { "additionalProperties": true, "type": "object" }, "severity": { "enum": [ "low", "medium", "high", "critical" ], "type": "string" }, "timestamp": { "format": "date-time", "type": "string" }, "violationType": { "enum": [ "bias", "privacy", "security", "robustness", "explainability", "other" ], "type": "string" } }, "required": [ "violationType", "severity", "description", "timestamp" ], "type": "object" }, "name": "safety_violation_incident_logger", "outputSchema": { "properties": { "complianceCategory": { "type": "string" }, "incidentId": { "type": "string" }, "nistReference": { "type": "string" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Architecte Sales Enablement — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Spendesk — 45 reps · attainment 67% · ramp 5 mois → 3 mois · programme 8 modules · +€2,1M ARR. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "averageDealSizeEur": { "type": "number" }, "name": { "type": "string" }, "productName": { "type": "string" }, "salesCycleMonths": { "type": "number" }, "sector": { "type": "string" }, "teamSize": { "type": "number" } }, "required": [ "name", "sector", "productName", "averageDealSizeEur", "salesCycleMonths", "teamSize" ], "type": "object" }, "currentEnablement": { "properties": { "battlecardsExist": { "type": "boolean" }, "contentLibraryExists": { "type": "boolean" }, "crmName": { "type": "string" }, "hasCrm": { "type": "boolean" }, "hasOnboarding": { "type": "boolean" }, "hasPlaybook": { "type": "boolean" }, "onboardingDurationWeeks": { "type": "number" }, "trainingFrequency": { "enum": [ "never", "quarterly", "monthly", "weekly" ], "type": "string" } }, "required": [ "hasPlaybook", "hasCrm", "hasOnboarding", "trainingFrequency", "contentLibraryExists", "battlecardsExist" ], "type": "object" }, "gaps": { "items": { "type": "string" }, "type": "array" }, "objectives": { "items": { "type": "string" }, "type": "array" }, "salesTeam": { "properties": { "aes": { "type": "number" }, "avgQuotaAttainmentPct": { "type": "number" }, "avgRampMonths": { "type": "number" }, "avgTimeOnAdminPct": { "type": "number" }, "bottomPerformerPct": { "type": "number" }, "sdrs": { "type": "number" }, "topPerformerPct": { "type": "number" }, "totalReps": { "type": "number" } }, "required": [ "totalReps", "sdrs", "aes", "avgRampMonths", "avgQuotaAttainmentPct", "avgTimeOnAdminPct", "topPerformerPct", "bottomPerformerPct" ], "type": "object" } }, "required": [ "company", "salesTeam", "currentEnablement", "gaps", "objectives" ], "type": "object" }, "name": "sales_enablement_architect", "outputSchema": null }, { "description": "Prévision de pipeline commercial — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Doctolib Enterprise — pipeline Q2 2026 · 50 deals enterprise/mid-market · forecast confidence par deal + commit/best-case/worst-case. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "arrCurrentEur": { "minimum": 0, "type": "number" }, "fte": { "minimum": 1, "type": "integer" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "quotaTeamEur": { "minimum": 0, "type": "number" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "fte" ], "type": "object" }, "focus": { "maxLength": 400, "type": "string" }, "historicalConversionByStage": { "items": { "properties": { "avgDays": { "minimum": 0, "type": "number" }, "conversionPct": { "maximum": 100, "minimum": 0, "type": "number" }, "fromStage": { "enum": [ "discovery", "qualification", "demo", "proposal", "negotiation", "closed-won", "closed-lost" ], "type": "string" }, "toStage": { "enum": [ "discovery", "qualification", "demo", "proposal", "negotiation", "closed-won", "closed-lost" ], "type": "string" } }, "required": [ "fromStage", "toStage", "avgDays", "conversionPct" ], "type": "object" }, "maxItems": 10, "type": "array" }, "pipeline": { "properties": { "deals": { "items": { "properties": { "account": { "maxLength": 160, "minLength": 2, "type": "string" }, "amountEur": { "minimum": 0, "type": "number" }, "championIdentified": { "type": "boolean" }, "competitor": { "maxLength": 120, "type": "string" }, "currentStage": { "enum": [ "discovery", "qualification", "demo", "proposal", "negotiation", "closed-won", "closed-lost" ], "type": "string" }, "daysInCurrentStage": { "minimum": 0, "type": "integer" }, "dealId": { "maxLength": 80, "minLength": 2, "type": "string" }, "economicBuyerAccess": { "enum": [ "direct", "via-champion", "blocked", "unknown" ], "type": "string" }, "expectedCloseDate": { "type": "string" }, "knownRisks": { "maxLength": 400, "type": "string" }, "ownerRep": { "maxLength": 80, "minLength": 2, "type": "string" }, "productsScope": { "items": { "type": "string" }, "maxItems": 8, "type": "array" }, "segment": { "enum": [ "enterprise", "mid-market", "smb", "self-serve" ], "type": "string" } }, "required": [ "dealId", "account", "segment", "amountEur", "currentStage", "daysInCurrentStage", "expectedCloseDate", "ownerRep" ], "type": "object" }, "maxItems": 60, "minItems": 3, "type": "array" }, "period": { "maxLength": 40, "minLength": 4, "type": "string" } }, "required": [ "period", "deals" ], "type": "object" } }, "required": [ "company", "pipeline" ], "type": "object" }, "name": "sales_pipeline_forecast", "outputSchema": null }, { "description": "Screening Sanctions Multi-listes — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Answers: For <entity>, run full OFAC + EU + UK HMT + UN + SECO + Canada SEMA + PEP + adverse media screening with composite risk score and evidence trail. · Is <company/individual> on any major international sanctions list? · What is the composite AML risk score for <entity> across all major watchlists? · Screen this M&A target / supplier / LP against all major sanctions lists and give me a compliance recommendation. · Is <entity> a PEP or associated with a PEP? What Enhanced Due Diligence is required? Reference case: Veridian Trading Co. LLC (Cyprus) — 7 listes · PEP check · adverse media 2 ans · composite 52/100 · escalate-to-compliance → EDD requis. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "address": { "maxLength": 400, "type": "string" }, "adverse_media_lookback_days": { "default": 365, "maximum": 3650, "minimum": 30, "type": "integer" }, "aliases": { "items": { "maxLength": 200, "type": "string" }, "maxItems": 10, "type": "array" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "context_note": { "maxLength": 500, "type": "string" }, "country_of_registration": { "maxLength": 100, "type": "string" }, "date_of_birth": { "type": "string" }, "entity_name": { "maxLength": 300, "minLength": 2, "type": "string" }, "entity_type": { "enum": [ "individual", "company", "vessel", "aircraft", "other" ], "type": "string" }, "jurisdiction_focus": { "default": "all", "enum": [ "us", "eu", "uk", "un", "ch", "ca", "all" ], "type": "string" } }, "required": [ "entity_type", "entity_name", "jurisdiction_focus", "adverse_media_lookback_days" ], "type": "object" }, "name": "sanctions_screener_multi", "outputSchema": null }, { "description": "Plans de sauvetage clients — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Kyriba — Plan sauvetage 30j · ARR €11.988 · Champion parti · Script 6 actions · 3 concessions. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "account": { "properties": { "arrEur": { "minimum": 0, "type": "number" }, "championName": { "maxLength": 100, "minLength": 2, "type": "string" }, "churnReason": { "maxLength": 300, "minLength": 10, "type": "string" }, "daysToChurn": { "maximum": 365, "minimum": 0, "type": "number" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "signals": { "items": { "maxLength": 200, "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" } }, "required": [ "name", "arrEur", "churnReason", "signals" ], "type": "object" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "product": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" } }, "required": [ "name" ], "type": "object" } }, "required": [ "company", "account", "product" ], "type": "object" }, "name": "save_plays", "outputSchema": null }, { "description": "Recherche bibliographique multi-sources sur la litterature scientifique. Sources : OpenAlex (200M+ works) · Semantic Scholar · arXiv · PubMed · CrossRef. Modes : search | meta_analysis | citation_network | expert_finder. Keyless / free tier. Cache LRU 12h.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "date_from": { "description": "Date ISO debut (YYYY-MM-DD)", "type": "string" }, "date_to": { "description": "Date ISO fin (YYYY-MM-DD)", "type": "string" }, "domain": { "description": "Domaine scientifique. Defaut: all", "enum": [ "biomedical", "physics", "cs", "chemistry", "social", "all" ], "type": "string" }, "max_results": { "description": "5-50. Defaut: 20", "type": "number" }, "min_citations": { "description": "Nombre minimal de citations", "type": "number" }, "mode": { "description": "Mode de recherche. Defaut: search", "enum": [ "search", "meta_analysis", "citation_network", "expert_finder" ], "type": "string" }, "query": { "description": "Keywords, titre, auteur, DOI (ex: 10.xxxx/xxxx accepte)", "type": "string" } }, "required": [ "query" ], "type": "object" }, "name": "sci_literature_search", "outputSchema": { "properties": { "citation_network": { "type": "object" }, "experts": { "type": "array" }, "meta_analysis": { "type": "object" }, "mode": { "type": "string" }, "papers": { "items": { "properties": { "abstract": { "type": "string" }, "authors": { "type": "array" }, "citations_count": { "type": "number" }, "doi": { "type": "string" }, "fields_of_study": { "items": { "type": "string" }, "type": "array" }, "open_access": { "type": "boolean" }, "pdf_url": { "type": "string" }, "published_date": { "type": "string" }, "source": { "enum": [ "arxiv", "semantic_scholar", "pubmed", "openalex", "crossref" ], "type": "string" }, "title": { "type": "string" }, "venue": { "type": "string" } }, "required": [ "title", "authors", "published_date", "source" ], "type": "object" }, "type": "array" }, "quality_score": { "type": "number" }, "query": { "type": "string" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "unavailable", "partial" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" } }, "required": [ "query", "mode", "papers", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Décodeur de filing SEC — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Answers: Read the 10-K of <ticker> and give me the material red flags, KPI movements, and a board-ready executive summary. · What has materially changed in <ticker>'s risk profile in its latest annual filing? Flag any going-concern or auditor-change signals. · Is there any M&A signal or strategic review hint in <ticker>'s most recent SEC filings? What's the evidence? · Prepare a due-diligence SEC filing brief for <ticker>: financial snapshot, red flags, governance changes, and recommended next actions. · What is the sentiment of <ticker>'s latest 10-K compared to its most recent 10-Q — bullish, neutral, or bearish? Reference case: SHOP · 10-K FY2024 · 4 red flags (1 critical: merchant concentration) · Revenue +24.7% YoY · . Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "cik": { "maxLength": 10, "minLength": 1, "type": "string" }, "filing_types": { "default": [ "10-K" ], "items": { "enum": [ "10-K", "10-Q", "8-K", "DEF-14A", "S-1", "all" ], "type": "string" }, "maxItems": 6, "minItems": 1, "type": "array" }, "focus": { "default": "all", "enum": [ "financial-health", "red-flags", "m&a-clues", "governance", "all" ], "type": "string" }, "lookback_months": { "default": 12, "maximum": 60, "minimum": 1, "type": "integer" }, "ticker": { "maxLength": 5, "minLength": 1, "type": "string" } }, "required": [ "filing_types", "lookback_months", "focus" ], "type": "object" }, "name": "sec_filing_decoder", "outputSchema": null }, { "description": "Pulse Média & Sentiment — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Answers: What is the current PR / brand sentiment for <company> over the last 7 days? Show top headlines, trend signals, and recommended actions. · Is there a crisis building for <brand>? Detect early-warning signals in press coverage and flag emerging negative narratives. · Track launch media coverage for <product> — what is the press sentiment and which topics dominate the conversation? · Compare media sentiment between <company> and its competitors over the past week. · What should our communications director prioritize in the next 48h based on current press coverage of <brand>? Reference case: Velora Payments — Pulse média 7j · sentiment neutre (score +5) · crise émergente détectée · . Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "date_range_days": { "default": 7, "maximum": 30, "minimum": 1, "type": "integer" }, "entity_name": { "maxLength": 200, "minLength": 2, "type": "string" }, "entity_type": { "default": "company", "enum": [ "company", "product", "person", "topic", "brand" ], "type": "string" }, "include_competitors": { "items": { "maxLength": 100, "type": "string" }, "maxItems": 5, "type": "array" }, "language_filter": { "default": "en", "enum": [ "en", "fr", "es", "de", "all" ], "type": "string" }, "sentiment_lens": { "default": "reputation", "enum": [ "reputation", "crisis-detection", "launch-monitoring", "general" ], "type": "string" } }, "required": [ "entity_name", "entity_type", "date_range_days", "language_filter", "sentiment_lens" ], "type": "object" }, "name": "sentiment_news_pulse", "outputSchema": null }, { "description": "Full SEO + CRO audit of any public URL. Analyses technical SEO (HTTP status, HTTPS, title/meta/canonical/robots, H1-H2, JSON-LD structured data, sitemap, robots.txt, OG/Twitter cards), content SEO (word count, keyword density top-10, readability estimate, image alt coverage, internal/external links), performance signals (page size, estimated render time, inline scripts/styles, unoptimised images), and CRO (CTA detection, above-fold CTAs, forms, social proof, trust signals, pricing visibility). Optionally compares up to 5 competitor URLs. Returns 0-100 scores per dimension plus a prioritised (P0/P1/P2) recommendation list. ICP: marketing managers, SEO/CRO consultants, e-commerce ops, agency teams. Budget: 8s per URL. Cache TTL: 1h.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "compare_competitors": { "description": "Optional list of competitor URLs to compare (max 5)", "items": { "type": "string" }, "maxItems": 5, "type": "array" }, "mode": { "description": "Audit scope — defaults to 'full'", "enum": [ "full", "seo_only", "cro_only", "technical_only", "content_only", "performance_only" ], "type": "string" }, "url": { "description": "Fully-qualified URL to audit (e.g. https://stripe.com/pricing)", "type": "string" } }, "required": [ "url" ], "type": "object" }, "name": "seo_cro_audit", "outputSchema": { "properties": { "audit_modes": { "items": { "type": "string" }, "type": "array" }, "competitor_comparison": { "type": "array" }, "content_seo": { "type": "object" }, "cro_signals": { "type": "object" }, "overall_scores": { "properties": { "combined": { "type": "number" }, "content": { "type": "number" }, "cro": { "type": "number" }, "performance": { "type": "number" }, "technical": { "type": "number" } }, "type": "object" }, "performance_signals": { "type": "object" }, "quality_score": { "type": "number" }, "recommendations": { "type": "array" }, "sources": { "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "technical_seo": { "type": "object" }, "url": { "type": "string" } }, "required": [ "url", "audit_modes", "technical_seo", "content_seo", "performance_signals", "cro_signals", "overall_scores", "recommendations", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "SEO keyword research from a seed keyword or topic. Uses Google Suggest (public, keyless) to discover related queries at 2 expansion levels, then clusters them by intent: informational / commercial / transactional / navigational — via heuristic pattern matching. Search volume is bucketed (very_high / high / medium / low / very_low) and clearly labelled as ESTIMATED — no fabricated precise numbers. Returns all keywords, intent clusters, quality scores (0-100), and top 10 opportunities. Supports country (gl) and language (hl) targeting. 100% keyless. Cache TTL 6h. ICP: SEO managers, content strategists, SaaS founders, agency teams.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "country": { "description": "ISO 3166-1 alpha-2 country code for Google Suggest (e.g. 'US', 'FR', 'DE'). Defaults to 'US'.", "type": "string" }, "language": { "description": "BCP-47 language code for suggestions (e.g. 'en', 'fr', 'de', 'es'). Defaults to 'en'.", "type": "string" }, "seed_keyword": { "description": "The seed keyword or topic to research (e.g. 'invoice software', 'project management tool')", "type": "string" } }, "required": [ "seed_keyword" ], "type": "object" }, "name": "seo_keyword_research", "outputSchema": { "properties": { "all_keywords": { "type": "array" }, "clusters": { "items": { "properties": { "count": { "type": "number" }, "intent": { "enum": [ "informational", "commercial", "transactional", "navigational" ], "type": "string" }, "keywords": { "type": "array" } }, "type": "object" }, "type": "array" }, "country": { "type": "string" }, "language": { "type": "string" }, "quality_score": { "type": "number" }, "seed_keyword": { "type": "string" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "top_opportunities": { "type": "array" }, "total_keywords": { "type": "number" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "seed_keyword", "country", "language", "total_keywords", "clusters", "all_keywords", "top_opportunities", "warnings", "quality_score", "status" ], "type": "object" } }, { "description": "Sharia compliance screening engine for Islamic banks, Sukuk issuers, Gulf sovereign funds, halal investment managers and MENA family offices. Zero competing MCP on this vertical.\n\nStandards supported: AAOIFI (default) | MSCI_Islamic | S&P_Sharia | DJIM\n\nFour modes:\n• company — Full Sharia screen of a listed company: business activity (halal/haram/mixed) + AAOIFI financial ratios (debt/market-cap <30%, interest-assets <30%, non-compliant revenue <5%)\n• instrument — Sukuk / halal fund classification by ISIN or name. Maps to known Sharia boards.\n• sector_screen — Industry classification (halal/haram/mixed) with rationale + examples. Static AAOIFI-based map covering 40+ sectors.\n• financial_ratios — AAOIFI ratio computation on fetched or provided financials.\n\nProhibited activities screened: alcohol, gambling, pork, weapons, pornography, tobacco, conventional banking (riba), conventional insurance, adult entertainment, embryonic stem cells.\n\nOutput includes compliance_status (halal/haram/doubtful_mixed/purification_required), purification_pct when applicable, P0/P1/P2 signals, quality_score, and sources.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "mode": { "description": "Screening mode. company=full listed company screen, instrument=Sukuk/fund classification, sector_screen=industry halal/haram classification, financial_ratios=AAOIFI ratio check.", "enum": [ "company", "instrument", "sector_screen", "financial_ratios" ], "type": "string" }, "query": { "description": "Entity to screen. Company name, ticker or ISIN (e.g. \"Aramco\", \"AAPL\", \"tobacco\", \"XS1234567890\").", "maxLength": 200, "minLength": 2, "type": "string" }, "standard": { "description": "Sharia standard to apply. Default \"AAOIFI\" (most conservative, widely accepted by Islamic banks).", "enum": [ "AAOIFI", "MSCI_Islamic", "S&P_Sharia", "DJIM" ], "type": "string" } }, "required": [ "mode", "query" ], "type": "object" }, "name": "sharia_compliance_screener", "outputSchema": { "properties": { "company": { "properties": { "aaoifi_reference": { "type": "string" }, "business_screen": { "properties": { "primary_activity_halal": { "type": "boolean" }, "prohibited_activities_detected": { "items": { "type": "string" }, "type": "array" } }, "required": [ "primary_activity_halal", "prohibited_activities_detected" ], "type": "object" }, "compliance_status": { "enum": [ "halal", "haram", "doubtful_mixed", "purification_required" ], "type": "string" }, "financial_screen": { "properties": { "cash_to_market_cap_pct": { "type": "number" }, "debt_to_market_cap_pct": { "type": "number" }, "interest_bearing_assets_pct": { "type": "number" }, "non_compliant_revenue_pct": { "type": "number" } }, "type": "object" }, "last_audit_date": { "type": "string" }, "name": { "type": "string" }, "purification_pct": { "type": "number" }, "sharia_compliant": { "type": "boolean" } }, "required": [ "name", "sharia_compliant", "compliance_status", "business_screen", "financial_screen" ], "type": "object" }, "instrument": { "properties": { "isin": { "type": "string" }, "issuer": { "type": "string" }, "rating": { "type": "string" }, "sharia_board": { "items": { "type": "string" }, "type": "array" }, "type": { "enum": [ "sukuk", "halal_fund", "murabaha", "ijara", "musharaka", "unclassified" ], "type": "string" }, "underlying_assets": { "type": "string" } }, "required": [ "type", "issuer" ], "type": "object" }, "mode": { "type": "string" }, "quality_score": { "maximum": 100, "minimum": 0, "type": "number" }, "sector_screen": { "properties": { "classification": { "enum": [ "halal", "haram", "mixed" ], "type": "string" }, "examples_halal": { "items": { "type": "string" }, "type": "array" }, "examples_haram": { "items": { "type": "string" }, "type": "array" }, "rationale": { "type": "string" }, "sector": { "type": "string" } }, "required": [ "sector", "classification", "rationale", "examples_halal", "examples_haram" ], "type": "object" }, "signals": { "items": { "properties": { "category": { "type": "string" }, "message": { "type": "string" }, "severity": { "enum": [ "P0", "P1", "P2" ], "type": "string" } }, "required": [ "severity", "category", "message" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "partial", "unavailable" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "standard_used": { "type": "string" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" } }, "required": [ "mode", "standard_used", "signals", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Tracks hourly social engagement velocity (likes, shares, comments) across Twitter, LinkedIn, and Reddit for CMOs. Inputs include platform handles/subreddits and time range. Outputs engagement metrics, velocity trends, and platform-specific insights. Ideal for real-time marketing performance monitoring and competitive benchmarking. Keywords: social media analytics, engagement tracking, marketing KPIs, CMO dashboard.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "hours": { "default": 24, "maximum": 72, "minimum": 1, "type": "number" }, "platforms": { "items": { "properties": { "handle": { "type": "string" }, "name": { "enum": [ "twitter", "linkedin", "reddit" ], "type": "string" }, "subreddit": { "type": "string" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "platforms" ], "type": "object" }, "name": "social_engagement_velocity_tracker", "outputSchema": { "properties": { "engagement": { "properties": { "linkedin": { "properties": { "comments": { "type": "number" }, "likes": { "type": "number" }, "shares": { "type": "number" }, "velocity": { "type": "number" } }, "type": "object" }, "reddit": { "properties": { "comments": { "type": "number" }, "upvotes": { "type": "number" }, "velocity": { "type": "number" } }, "type": "object" }, "twitter": { "properties": { "likes": { "type": "number" }, "replies": { "type": "number" }, "retweets": { "type": "number" }, "velocity": { "type": "number" } }, "type": "object" } }, "type": "object" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "trends": { "items": { "properties": { "engagement": { "type": "number" }, "platform": { "type": "string" }, "time": { "format": "date-time", "type": "string" } }, "type": "object" }, "type": "array" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Analyzes up to 10 social media influencers for fake followers by checking engagement velocity patterns (Trends24) and RSS feed anomalies. Returns authenticity scores, follower growth spikes, and suspicious activity flags. Optimized for CMOs evaluating influencer partnerships. Includes keywords: influencer marketing, fake follower detection, engagement analysis, social media audit.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "influencerHandles": { "description": "Array of up to 10 social media handles (e.g., ['@influencer1', 'user2'])", "items": { "type": "string" }, "maxItems": 10, "type": "array" }, "platform": { "description": "Social media platform of the influencers", "enum": [ "instagram", "twitter", "tiktok", "youtube" ], "type": "string" } }, "required": [ "influencerHandles", "platform" ], "type": "object" }, "name": "social_influencer_fake_follower_detector", "outputSchema": { "properties": { "results": { "items": { "properties": { "authenticityScore": { "maximum": 100, "minimum": 0, "type": "number" }, "engagementVelocity": { "type": "number" }, "fakeFollowerPercentage": { "maximum": 100, "minimum": 0, "type": "number" }, "growthSpikes": { "items": { "properties": { "date": { "format": "date", "type": "string" }, "followersGained": { "type": "number" } }, "type": "object" }, "type": "array" }, "handle": { "type": "string" }, "suspiciousActivity": { "items": { "properties": { "details": { "type": "string" }, "timestamp": { "format": "date-time", "type": "string" }, "type": { "type": "string" } }, "type": "object" }, "type": "array" } }, "required": [ "handle", "authenticityScore", "fakeFollowerPercentage" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "source_type": { "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "summary": { "properties": { "averageAuthenticityScore": { "type": "number" }, "highestFakeFollowerPercentage": { "type": "number" }, "totalSuspiciousActivities": { "type": "number" } }, "type": "object" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status", "results", "warnings", "sources" ], "type": "object" } }, { "description": "Estimates financial impact of a data breach across three jurisdictions (US, EU, UK) for CFO strategic planning. Inputs include breach size, industry sector, and affected jurisdictions. Outputs include direct costs, regulatory fines, reputational damage, and cyber insurance premium adjustments. Ideal for cross-border risk assessment, financial contingency planning, and board-level reporting. Keywords: data breach cost, regulatory fines, cyber insurance, financial risk, cross-jurisdiction impact.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "detection_time_days": { "description": "Time in days to detect the breach", "minimum": 1, "type": "number" }, "industry": { "description": "Industry sector of the affected organization", "enum": [ "healthcare", "financial", "retail", "technology", "other" ], "type": "string" }, "jurisdictions": { "description": "Jurisdictions where the breach has legal or financial impact", "items": { "enum": [ "US", "EU", "UK" ], "type": "string" }, "maxItems": 3, "minItems": 1, "type": "array" }, "records_lost": { "description": "Number of records compromised in the breach", "minimum": 1, "type": "number" } }, "required": [ "records_lost", "jurisdictions" ], "type": "object" }, "name": "sovereign_data_breach_impact", "outputSchema": { "properties": { "cost_per_record_usd": { "description": "Cost per compromised record in USD", "type": "number" }, "cyber_insurance_impact": { "properties": { "current_premium_usd": { "type": "number" }, "premium_increase_pct": { "type": "number" }, "projected_premium_usd": { "type": "number" } }, "type": "object" }, "regulatory_fines_usd": { "properties": { "EU": { "type": "number" }, "UK": { "type": "number" }, "US": { "type": "number" } }, "type": "object" }, "reputational_damage_usd": { "description": "Estimated reputational damage cost in USD", "type": "number" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "total_cost_usd": { "description": "Estimated total financial impact in USD", "type": "number" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "As a CTO, predict potential SLO breaches 24 hours in advance by analyzing public incident reports and MITRE ATT&CK techniques. Input your service's critical components and reliability thresholds to receive breach probability scores, top contributing TTPs, and recommended mitigations. Uses MITRE ATT&CK, GitHub Advisories, and Cloudflare Radar data. Pass async:true to avoid timeout.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "service_components": { "items": { "properties": { "name": { "type": "string" }, "slo_threshold": { "type": "number" }, "tags": { "items": { "type": "string" }, "type": "array" } }, "required": [ "name", "slo_threshold" ], "type": "object" }, "type": "array" }, "time_window_hours": { "default": 24, "type": "number" } }, "required": [ "service_components" ], "type": "object" }, "name": "sre_slo_breach_predictor", "outputSchema": { "properties": { "breach_probability": { "type": "number" }, "incident_reports": { "items": { "properties": { "id": { "type": "string" }, "severity": { "type": "string" }, "source": { "type": "string" }, "title": { "type": "string" } }, "type": "object" }, "type": "array" }, "recommended_actions": { "items": { "type": "string" }, "type": "array" }, "sources": { "items": { "properties": { "fetched_at": { "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "top_ttp_contributors": { "items": { "properties": { "probability_impact": { "type": "number" }, "technique_id": { "type": "string" }, "technique_name": { "type": "string" } }, "type": "object" }, "type": "array" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Analyseur d'options stratégiques — Gapup agent-payable C-suite expertise (CSO). Returns a structured, audited deliverable. Reference case: Aircall — 5 options stratégiques post-Série D (2023-2024). Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "countryHQ": { "maxLength": 60, "minLength": 2, "type": "string" }, "currentArrEur": { "minimum": 0, "type": "number" }, "foundedYear": { "maximum": 2030, "minimum": 1990, "type": "integer" }, "headcountNow": { "minimum": 0, "type": "integer" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "pitch": { "maxLength": 800, "minLength": 20, "type": "string" }, "stage": { "enum": [ "pre-seed", "seed", "series-a", "series-b", "series-c", "growth", "public" ], "type": "string" }, "url": { "format": "uri", "type": "string" } }, "required": [ "name", "pitch", "stage", "countryHQ", "foundedYear", "currentArrEur", "headcountNow" ], "type": "object" }, "founderConstraints": { "properties": { "maxCashRequiredEur": { "minimum": 0, "type": "number" }, "maxDilutionPct": { "maximum": 100, "minimum": 0, "type": "number" }, "mustRetainControl": { "type": "boolean" }, "noGoConditions": { "items": { "maxLength": 200, "minLength": 5, "type": "string" }, "maxItems": 5, "type": "array" }, "timeToFirstValidationWeeks": { "maximum": 52, "minimum": 4, "type": "integer" } }, "required": [ "maxCashRequiredEur", "maxDilutionPct", "mustRetainControl", "timeToFirstValidationWeeks" ], "type": "object" }, "optionHypotheses": { "items": { "properties": { "hypothesis": { "maxLength": 400, "minLength": 20, "type": "string" }, "id": { "maxLength": 60, "minLength": 2, "type": "string" }, "label": { "maxLength": 120, "minLength": 5, "type": "string" } }, "required": [ "id", "label", "hypothesis" ], "type": "object" }, "maxItems": 6, "minItems": 3, "type": "array" }, "strategicContext": { "properties": { "burnMonthlyEur": { "minimum": 0, "type": "number" }, "cashEur": { "minimum": 0, "type": "number" }, "cashRunwayMonths": { "minimum": 0, "type": "number" }, "coreCompetencies": { "items": { "maxLength": 200, "minLength": 5, "type": "string" }, "maxItems": 6, "minItems": 2, "type": "array" }, "triggerEvent": { "maxLength": 500, "minLength": 20, "type": "string" }, "windowOpportunityMonths": { "maximum": 36, "minimum": 3, "type": "integer" } }, "required": [ "triggerEvent", "windowOpportunityMonths", "cashRunwayMonths", "cashEur", "burnMonthlyEur", "coreCompetencies" ], "type": "object" } }, "required": [ "company", "strategicContext", "optionHypotheses", "founderConstraints" ], "type": "object" }, "name": "strategic_options_analyzer", "outputSchema": null }, { "description": "Audit ESG des fournisseurs — Gapup agent-payable C-suite expertise (SUSTAINABILITY). Returns a structured, audited deliverable. Reference case: TechCorp — Audit ESG fournisseurs 2025 (5 fournisseurs, €1.37M spend). Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "auditCriteria": { "properties": { "environmental": { "items": { "maxLength": 200, "minLength": 3, "type": "string" }, "maxItems": 8, "minItems": 2, "type": "array" }, "governance": { "items": { "maxLength": 200, "minLength": 3, "type": "string" }, "maxItems": 8, "minItems": 2, "type": "array" }, "social": { "items": { "maxLength": 200, "minLength": 3, "type": "string" }, "maxItems": 8, "minItems": 2, "type": "array" }, "supplyChain": { "items": { "maxLength": 200, "minLength": 3, "type": "string" }, "maxItems": 8, "minItems": 2, "type": "array" } }, "required": [ "environmental", "social", "governance", "supplyChain" ], "type": "object" }, "company": { "properties": { "country": { "maxLength": 80, "minLength": 2, "type": "string" }, "fte": { "minimum": 1, "type": "integer" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "revenueEur": { "minimum": 0, "type": "number" }, "sector": { "maxLength": 160, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "fte", "revenueEur", "country" ], "type": "object" }, "focus": { "maxLength": 500, "type": "string" }, "suppliers": { "items": { "properties": { "annualSpendEur": { "minimum": 0, "type": "number" }, "country": { "maxLength": 80, "minLength": 2, "type": "string" }, "existingCertifications": { "items": { "type": "string" }, "type": "array" }, "knownIssues": { "maxLength": 400, "type": "string" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" }, "tier": { "enum": [ "Tier 1", "Tier 2", "Tier 3" ], "type": "string" } }, "required": [ "name", "sector", "country", "annualSpendEur", "tier" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "targetScore": { "maximum": 100, "minimum": 0, "type": "integer" } }, "required": [ "company", "suppliers", "auditCriteria" ], "type": "object" }, "name": "supplier_esg_audit", "outputSchema": null }, { "description": "Provides real-time foreign exchange exposure dashboard for supply chain monitoring. Designed for COO persona to track currency risk across suppliers and regions. Inputs include supplier IDs, base currency, and target currencies. Outputs structured FX exposure data with risk indicators, exchange rates, and supplier impact analysis sourced from World Bank LPI and live FX rate APIs.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "baseCurrency": { "description": "Base currency code (ISO 4217) for exposure calculation", "pattern": "^[A-Z]{3}$", "type": "string" }, "riskThreshold": { "description": "Percentage threshold for high-risk exposure flagging", "maximum": 100, "minimum": 0, "type": "number" }, "supplierIds": { "description": "List of supplier identifiers to analyze", "items": { "type": "string" }, "type": "array" }, "targetCurrencies": { "description": "Target currency codes (ISO 4217) to compare against base", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "baseCurrency", "targetCurrencies" ], "type": "object" }, "name": "supply_chain_fx_exposure_dashboard", "outputSchema": { "properties": { "data": { "properties": { "baseCurrency": { "type": "string" }, "summary": { "properties": { "highRiskCount": { "type": "integer" }, "lastUpdated": { "format": "date-time", "type": "string" }, "lowRiskCount": { "type": "integer" }, "mediumRiskCount": { "type": "integer" }, "totalExposure": { "type": "number" } }, "required": [ "totalExposure", "highRiskCount", "mediumRiskCount", "lowRiskCount" ], "type": "object" }, "suppliers": { "items": { "properties": { "country": { "type": "string" }, "currency": { "type": "string" }, "exposure": { "type": "number" }, "id": { "type": "string" }, "lastUpdated": { "format": "date-time", "type": "string" }, "riskLevel": { "enum": [ "low", "medium", "high" ], "type": "string" } }, "required": [ "id", "country", "currency", "exposure", "riskLevel" ], "type": "object" }, "type": "array" }, "targetCurrencies": { "items": { "properties": { "code": { "type": "string" }, "lastUpdated": { "format": "date-time", "type": "string" }, "rate": { "type": "number" } }, "required": [ "code", "rate" ], "type": "object" }, "type": "array" } }, "required": [ "baseCurrency", "targetCurrencies", "suppliers", "summary" ], "type": "object" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "name": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Rapport de durabilité — Gapup agent-payable C-suite expertise (SUSTAINABILITY). Returns a structured, audited deliverable. Reference case: GreenLoop Solutions — rapport durabilité B-Corp 2025 (95 FTE, €18M CA). Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "audienceProfile": { "enum": [ "investors", "B2B-clients", "talents", "regulators", "general-public", "mixed" ], "type": "string" }, "company": { "properties": { "country": { "maxLength": 80, "minLength": 2, "type": "string" }, "foundedYear": { "maximum": 2100, "minimum": 1800, "type": "integer" }, "fte": { "minimum": 1, "type": "integer" }, "mission": { "maxLength": 400, "minLength": 20, "type": "string" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "revenueEur": { "minimum": 0, "type": "number" }, "sector": { "maxLength": 160, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "fte", "revenueEur", "mission", "foundedYear", "country" ], "type": "object" }, "existingLabels": { "items": { "enum": [ "B-Corp", "EcoVadis Bronze", "EcoVadis Silver", "EcoVadis Gold", "EcoVadis Platinum", "Lucie 26000", "ISO 14001", "ISO 26000", "BSR" ], "type": "string" }, "type": "array" }, "focus": { "maxLength": 500, "type": "string" }, "pillars": { "properties": { "environmental": { "properties": { "actions": { "items": { "type": "string" }, "maxItems": 8, "minItems": 1, "type": "array" }, "ambitions": { "minLength": 10, "type": "string" }, "kpis": { "minLength": 10, "type": "string" } }, "required": [ "kpis", "actions", "ambitions" ], "type": "object" }, "governance": { "properties": { "actions": { "items": { "type": "string" }, "maxItems": 8, "minItems": 1, "type": "array" }, "ambitions": { "minLength": 10, "type": "string" }, "kpis": { "minLength": 10, "type": "string" } }, "required": [ "kpis", "actions", "ambitions" ], "type": "object" }, "social": { "properties": { "actions": { "items": { "type": "string" }, "maxItems": 8, "minItems": 1, "type": "array" }, "ambitions": { "minLength": 10, "type": "string" }, "kpis": { "minLength": 10, "type": "string" } }, "required": [ "kpis", "actions", "ambitions" ], "type": "object" } }, "required": [ "environmental", "social", "governance" ], "type": "object" }, "stakeholders": { "items": { "properties": { "engagement": { "maxLength": 500, "minLength": 10, "type": "string" }, "type": { "enum": [ "clients", "employés", "investisseurs", "fournisseurs", "communauté", "régulateurs" ], "type": "string" } }, "required": [ "type", "engagement" ], "type": "object" }, "maxItems": 8, "minItems": 2, "type": "array" }, "targetLabels": { "items": { "enum": [ "B-Corp", "EcoVadis Bronze", "EcoVadis Silver", "EcoVadis Gold", "EcoVadis Platinum", "Lucie 26000", "ISO 14001", "ISO 26000", "BSR" ], "type": "string" }, "type": "array" } }, "required": [ "company", "pillars", "stakeholders", "audienceProfile" ], "type": "object" }, "name": "sustainability_report", "outputSchema": null }, { "description": "Pilote de reporting durabilité — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Reference case: AlphaTech Industries SAS — premier rapport CSRD wave 2 (exercice 2025). Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "country": { "maxLength": 80, "minLength": 2, "type": "string" }, "fte": { "minimum": 1, "type": "integer" }, "listedStatus": { "enum": [ "listed", "private-large", "private-sme", "public-sector" ], "type": "string" }, "naceCode": { "type": "string" }, "name": { "maxLength": 120, "minLength": 2, "type": "string" }, "parentGroup": { "type": "string" }, "revenueEur": { "minimum": 0, "type": "number" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" } }, "required": [ "name", "sector", "fte", "revenueEur", "listedStatus", "country" ], "type": "object" }, "dataInputs": { "properties": { "environmental": { "properties": { "energyMixPct": { "type": "object" }, "scope1Tco2eq": { "type": "number" }, "scope2Tco2eqLocation": { "type": "number" }, "scope2Tco2eqMarket": { "type": "number" }, "scope3Tco2eq": { "type": "number" }, "wasteT": { "type": "number" }, "waterUseM3": { "type": "number" } }, "type": "object" }, "governance": { "type": "object" }, "social": { "properties": { "accidentsLTI": { "type": "number" }, "fteByGenderPct": { "type": "object" }, "payGapPct": { "type": "number" }, "trainingHoursAvg": { "type": "number" } }, "type": "object" }, "valueChain": { "properties": { "criticalRawMaterials": { "items": { "type": "string" }, "type": "array" }, "suppliersCount": { "type": "integer" } }, "type": "object" } }, "type": "object" }, "focus": { "maxLength": 500, "type": "string" }, "materiality": { "items": { "properties": { "financiallyMaterial": { "type": "boolean" }, "impactMaterial": { "type": "boolean" }, "justification": { "type": "string" }, "topic": { "type": "string" } }, "required": [ "topic", "financiallyMaterial", "impactMaterial", "justification" ], "type": "object" }, "maxItems": 30, "minItems": 3, "type": "array" }, "targetFrameworks": { "items": { "enum": [ "CSRD", "SFDR Article 8", "SFDR Article 9", "GRI", "TCFD", "ISSB IFRS S1/S2", "CDP" ], "type": "string" }, "maxItems": 7, "minItems": 1, "type": "array" } }, "required": [ "company", "targetFrameworks", "dataInputs", "materiality" ], "type": "object" }, "name": "sustainability_reporting_pilot", "outputSchema": null }, { "description": "Monitors syndicated loan covenants for potential breaches by analyzing Tradeweb market data. Designed for CFOs to proactively identify financial compliance risks in loan agreements. Accepts loan identifiers, covenant thresholds, and reporting period as inputs. Returns structured breach alerts with market context and severity indicators.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "covenantThresholds": { "properties": { "debtServiceCoverageRatio": { "type": "number" }, "interestCoverageRatio": { "type": "number" }, "leverageRatio": { "type": "number" } }, "required": [ "debtServiceCoverageRatio", "interestCoverageRatio", "leverageRatio" ], "type": "object" }, "currency": { "description": "ISO currency code for financial values", "type": "string" }, "loanId": { "description": "Unique identifier for the syndicated loan", "type": "string" }, "reportingPeriod": { "description": "Time period for covenant compliance check", "enum": [ "daily", "weekly", "monthly" ], "type": "string" } }, "required": [ "loanId", "covenantThresholds", "reportingPeriod" ], "type": "object" }, "name": "syndicated_loan_covenant_breach_alert", "outputSchema": { "properties": { "breaches": { "items": { "properties": { "covenantType": { "type": "string" }, "currentValue": { "type": "number" }, "marketContext": { "type": "string" }, "severity": { "enum": [ "low", "medium", "high" ], "type": "string" }, "thresholdValue": { "type": "number" } }, "required": [ "covenantType", "currentValue", "thresholdValue", "severity" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Provides CFOs with peer benchmarking for syndicated loan pricing by comparing current loan terms against market data from Tradeweb and FRED. Inputs include loan amount, tenor, credit rating, and currency. Outputs structured pricing benchmarks with spread, yield, and fee comparisons. Ideal for quick validation of loan competitiveness or negotiation preparation.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "creditRating": { "description": "Borrower credit rating (e.g., 'BBB', 'BB+')", "type": "string" }, "currency": { "description": "Currency code (e.g., 'USD', 'EUR')", "type": "string" }, "loanAmount": { "description": "Loan amount in millions", "type": "number" }, "region": { "description": "Region for benchmarking (e.g., 'US', 'EU')", "type": "string" }, "tenor": { "description": "Loan tenor (e.g., '5Y', '3Y')", "type": "string" } }, "required": [ "loanAmount", "tenor", "creditRating", "currency" ], "type": "object" }, "name": "syndicated_loan_pricing_benchmark", "outputSchema": { "properties": { "benchmarks": { "properties": { "fees": { "type": "number" }, "peers": { "items": { "properties": { "fees": { "type": "number" }, "name": { "type": "string" }, "spread": { "type": "number" }, "yield": { "type": "number" } }, "type": "object" }, "type": "array" }, "spread": { "type": "number" }, "yield": { "type": "number" } }, "type": "object" }, "sources": { "items": { "properties": { "data": { "type": "object" }, "fetched_at": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "For CHROs: analyzes employee contracts for non-compete, IP assignment, and confidentiality clauses, comparing against state labor laws and jurisdiction-specific precedents. Returns risk levels, conflicting statutes, and suggested revisions. Uses USPTO PatFT, CourtListener, and EUR-Lex for legal cross-referencing. Ideal for contract reviews, compliance audits, or policy updates.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "contract_text": { "description": "Full text of the employee contract or clause section to analyze", "type": "string" }, "effective_date": { "description": "Contract effective date (YYYY-MM-DD)", "format": "date", "type": "string" }, "employee_role": { "description": "Job title or role classification (e.g., 'Software Engineer', 'Executive')", "type": "string" }, "jurisdiction": { "description": "State or country jurisdiction (e.g., 'California', 'Germany')", "type": "string" } }, "required": [ "contract_text", "jurisdiction" ], "type": "object" }, "name": "talent_contract_risk_mapper", "outputSchema": { "properties": { "conflicting_statutes": { "items": { "properties": { "conflict_description": { "type": "string" }, "jurisdiction": { "type": "string" }, "statute_code": { "type": "string" }, "statute_name": { "type": "string" } }, "type": "object" }, "type": "array" }, "risk_summary": { "properties": { "confidentiality": { "enum": [ "high", "medium", "low", "compliant" ], "type": "string" }, "ip_assignment": { "enum": [ "high", "medium", "low", "compliant" ], "type": "string" }, "non_compete": { "enum": [ "high", "medium", "low", "compliant" ], "type": "string" } }, "type": "object" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "source_type": { "enum": [ "USPTO", "CourtListener", "EUR-Lex" ], "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "suggested_revisions": { "items": { "type": "string" }, "type": "array" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "HR tech intelligence for CHROs, recruiters, VC teams, comp & benefits leads and workforce planners. Four modes powered by ESCO, O*NET, BLS OES and crowd-sourced salary data:\n\n• salary_benchmark — cash-only salary medians (p25/median/p75) for 54+ roles across US/EU/Asia. Covers tech, finance, compliance, healthcare, marketing, ops and C-suite. Data from BLS OES, Levels.fyi and StackOverflow Developer Survey 2024.\n• skills_taxonomy — maps a skill to its ESCO URI, O*NET codes, skill type (hard/soft/knowledge/cert), 8 related skills with similarity scores and typical roles.\n• job_market_trends — YoY growth %, open positions estimate, top employers and leading skills per job category × country. Static 2024 data with BLS baseline fallback.\n• adjacent_roles — up to 6 roles adjacent to a source role with ESCO taxonomy adjacency: similarity score, salary delta % and skills overlap %.\n\nAll salary data is cash-only (excludes equity/RSU/bonus). Cache TTL: 24h (stable labour market data). Optional env ONET_API_KEY for authenticated O*NET lookups (free registration at onetcenter.org).", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "country": { "description": "ISO 2-letter country code. Default: US. Examples: US, FR, DE, GB, SG.", "maxLength": 5, "minLength": 2, "type": "string" }, "mode": { "description": "Analysis mode: salary_benchmark=compensation data, skills_taxonomy=ESCO/O*NET mapping, job_market_trends=market growth and demand, adjacent_roles=career path recommendations.", "enum": [ "salary_benchmark", "skills_taxonomy", "job_market_trends", "adjacent_roles" ], "type": "string" }, "role": { "description": "Job title (required for salary_benchmark, job_market_trends, adjacent_roles). Examples: \"Senior Software Engineer\", \"Compliance Officer\", \"Data Scientist\", \"CFO\".", "maxLength": 150, "minLength": 2, "type": "string" }, "seniority": { "description": "Seniority level. Default: senior. Affects salary benchmark ranges.", "enum": [ "junior", "mid", "senior", "lead", "principal", "executive" ], "type": "string" }, "skill": { "description": "Skill to classify (required for skills_taxonomy mode). Examples: \"Python\", \"transformer architecture\", \"GDPR\", \"Kubernetes\", \"leadership\".", "maxLength": 150, "minLength": 2, "type": "string" } }, "required": [ "mode" ], "type": "object" }, "name": "talent_intelligence", "outputSchema": { "properties": { "adjacent_roles": { "items": { "properties": { "role": { "type": "string" }, "salary_delta_pct": { "type": "number" }, "similarity": { "type": "number" }, "skills_overlap_pct": { "type": "number" } }, "required": [ "role", "similarity", "skills_overlap_pct" ], "type": "object" }, "type": "array" }, "job_market_trends": { "properties": { "category": { "type": "string" }, "country": { "type": "string" }, "growth_pct": { "type": "number" }, "leading_skills": { "items": { "type": "string" }, "type": "array" }, "open_positions_estimate": { "type": "number" }, "period_months": { "type": "number" }, "top_employers": { "items": { "type": "string" }, "type": "array" } }, "required": [ "category", "country", "period_months", "growth_pct", "open_positions_estimate", "top_employers", "leading_skills" ], "type": "object" }, "mode": { "type": "string" }, "quality_score": { "type": "number" }, "salary_benchmark": { "properties": { "cash_only": { "type": "boolean" }, "country": { "type": "string" }, "currency": { "type": "string" }, "last_update": { "type": "string" }, "median": { "type": "number" }, "p25": { "type": "number" }, "p75": { "type": "number" }, "role": { "type": "string" }, "sample_size": { "type": "number" }, "seniority": { "type": "string" }, "sources": { "items": { "type": "string" }, "type": "array" } }, "required": [ "role", "country", "seniority", "currency", "median", "p25", "p75", "cash_only", "sources", "last_update" ], "type": "object" }, "skills_taxonomy": { "properties": { "esco_id": { "type": "string" }, "onet_codes": { "items": { "type": "string" }, "type": "array" }, "related_skills": { "items": { "properties": { "name": { "type": "string" }, "similarity": { "type": "number" } }, "required": [ "name", "similarity" ], "type": "object" }, "type": "array" }, "skill": { "type": "string" }, "teachable": { "type": "boolean" }, "type": { "enum": [ "hard_skill", "soft_skill", "knowledge", "certification" ], "type": "string" }, "typical_in_roles": { "items": { "type": "string" }, "type": "array" } }, "required": [ "skill", "onet_codes", "type", "related_skills", "typical_in_roles", "teachable" ], "type": "object" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "partial", "unavailable" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" } }, "required": [ "mode", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Generates a real-time legal risk dashboard for CHROs, covering contracts, intellectual property, and labor law compliance. Inputs include jurisdiction, employee count, and risk thresholds; outputs include risk scores, actionable alerts, and source citations. Ideal for proactive legal risk management and compliance monitoring. Pass async:true to avoid timeout.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "employeeCount": { "minimum": 1, "type": "number" }, "includeIP": { "default": true, "type": "boolean" }, "includeLaborLaw": { "default": true, "type": "boolean" }, "jurisdiction": { "enum": [ "US", "EU", "US+EU" ], "type": "string" }, "riskThreshold": { "default": 70, "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "jurisdiction", "employeeCount" ], "type": "object" }, "name": "talent_legal_dashboard", "outputSchema": { "properties": { "alerts": { "items": { "properties": { "action": { "type": "string" }, "description": { "type": "string" }, "referenceId": { "type": "string" }, "severity": { "enum": [ "low", "medium", "high", "critical" ], "type": "string" }, "type": { "enum": [ "contract", "ip", "labor_law" ], "type": "string" } }, "required": [ "type", "severity", "description", "action" ], "type": "object" }, "type": "array" }, "lastUpdated": { "format": "date-time", "type": "string" }, "riskScore": { "maximum": 100, "minimum": 0, "type": "number" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "source": { "enum": [ "CourtListener", "USPTO PatFT", "EUR-Lex" ], "type": "string" }, "url": { "format": "uri", "type": "string" } }, "required": [ "url", "fetched_at", "source" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Estimates litigation exposure risk for CHROs by analyzing past employee lawsuits, settlement amounts, and industry benchmarks. Inputs include company location, industry code, and employee count range. Returns exposure score, average settlement amounts, lawsuit frequency trends, and risk factors. Ideal for legal risk assessment, HR strategy planning, and board-level reporting. Pass async:true to avoid timeout.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company_location": { "description": "State or region where company operates (e.g., 'CA', 'New York')", "type": "string" }, "employee_count": { "description": "Current number of employees", "minimum": 1, "type": "number" }, "industry_code": { "description": "NAICS industry code (e.g., '541511' for IT services)", "type": "string" }, "lookback_years": { "default": 5, "description": "Number of years to analyze", "maximum": 10, "minimum": 1, "type": "number" } }, "required": [ "company_location", "industry_code" ], "type": "object" }, "name": "talent_litigation_exposure", "outputSchema": { "properties": { "avg_settlement": { "description": "Average settlement amount in USD", "type": "number" }, "exposure_score": { "description": "Normalized risk score (0-100)", "maximum": 100, "minimum": 0, "type": "number" }, "historical_trend": { "enum": [ "increasing", "stable", "decreasing" ], "type": "string" }, "industry_benchmark": { "description": "Industry average exposure score", "type": "number" }, "lawsuit_frequency": { "description": "Lawsuits per 1000 employees per year", "type": "number" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "top_risk_factors": { "items": { "type": "string" }, "maxItems": 5, "type": "array" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status", "exposure_score", "warnings", "sources" ], "type": "object" } }, { "description": "Analyzes employee poaching risk for CHROs by evaluating LinkedIn profile activity (job searches, profile views) and comparing compensation against BLS benchmarks. Returns a ranked list of high-risk employees with risk scores and suggested retention actions. Ideal for proactive talent retention strategies. Keywords: employee retention, poaching risk, compensation benchmark, LinkedIn activity, CHRO analytics.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "benchmark_job_title": { "description": "Specific job title for compensation benchmarking", "type": "string" }, "department": { "description": "Department filter (e.g., 'Engineering', 'Sales')", "type": "string" }, "location": { "description": "Geographic location filter (e.g., 'San Francisco, CA')", "type": "string" }, "min_tenure_months": { "description": "Minimum tenure in months to include in analysis", "type": "number" } }, "required": [ "department" ], "type": "object" }, "name": "talent_poaching_risk", "outputSchema": { "properties": { "benchmark_comparison": { "properties": { "company_avg_salary": { "type": "number" }, "job_title": { "type": "string" }, "market_avg_salary": { "type": "number" }, "percentile": { "type": "number" } }, "type": "object" }, "department_avg_risk": { "type": "number" }, "risk_assessment": { "items": { "properties": { "comp_benchmark_delta": { "type": "number" }, "employee_id": { "type": "string" }, "last_profile_update": { "type": "string" }, "linkedin_activity_score": { "type": "number" }, "name": { "type": "string" }, "retention_suggestions": { "items": { "type": "string" }, "type": "array" }, "risk_score": { "type": "number" } }, "required": [ "employee_id", "risk_score" ], "type": "object" }, "type": "array" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "As a COO, identify tariff reclassification opportunities to reduce import costs. Analyzes product HS codes against WTO TFA and USA Trade Online data to find lower-duty classifications. Inputs: product description, current HS code, country of origin, and annual import volume. Outputs: potential duty savings, alternative HS codes, and compliance considerations.", "inputSchema": { "properties": { "annualVolume": { "minimum": 1, "type": "number" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "countryOfOrigin": { "type": "string" }, "currentDutyRate": { "maximum": 100, "minimum": 0, "type": "number" }, "currentHsCode": { "pattern": "^\\d{6,10}$", "type": "string" }, "productDescription": { "type": "string" } }, "required": [ "productDescription", "currentHsCode", "countryOfOrigin" ], "type": "object" }, "name": "tariff_arbitrage_finder", "outputSchema": { "properties": { "opportunities": { "items": { "properties": { "alternativeHsCode": { "type": "string" }, "complianceNotes": { "type": "string" }, "confidence": { "maximum": 1, "minimum": 0, "type": "number" }, "dutyRate": { "type": "number" }, "dutySavings": { "type": "number" } }, "required": [ "alternativeHsCode", "dutyRate", "dutySavings" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "fetched_at": { "type": "string" }, "name": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "As a COO, model how proposed tariff changes affect landed costs for imported goods. Inputs: HS code, current tariff rate, proposed tariff rate, product value, shipping cost, and country of origin. Outputs: detailed cost breakdown including new duties, taxes, and total landed cost impact. Sources include WTO TFA and US Census trade data.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "countryOfOrigin": { "maxLength": 2, "minLength": 2, "type": "string" }, "currentTariffRate": { "maximum": 100, "minimum": 0, "type": "number" }, "hsCode": { "maxLength": 10, "minLength": 6, "type": "string" }, "productValue": { "minimum": 0, "type": "number" }, "proposedTariffRate": { "maximum": 100, "minimum": 0, "type": "number" }, "shippingCost": { "minimum": 0, "type": "number" } }, "required": [ "hsCode", "currentTariffRate", "proposedTariffRate", "productValue", "countryOfOrigin" ], "type": "object" }, "name": "tariff_impact_simulator", "outputSchema": { "properties": { "costImpact": { "type": "number" }, "costImpactPercentage": { "type": "number" }, "currentDuty": { "type": "number" }, "currentLandedCost": { "type": "number" }, "dutyDifference": { "type": "number" }, "proposedDuty": { "type": "number" }, "proposedLandedCost": { "type": "number" }, "sources": { "items": { "properties": { "description": { "type": "string" }, "fetched_at": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Multi-jurisdiction tax compliance data for international SaaS, cross-border marketplaces and expat services. Five modes: (1) vat_lookup — validate EU VAT numbers live via VIES SOAP (27 EU countries) or UK VRN via HMRC; (2) sales_tax — US state sales tax rates, nexus thresholds (post-Wayfair 2018), digital goods taxability for all 50 states + DC; (3) gst — APAC GST/SST/consumption-tax rates for IN, SG, AU, NZ, MY, JP, KR, TH, ID, PH, VN with reduced rates and registration thresholds; (4) oss_ioss_eligibility — EU One-Stop-Shop and Import-OSS eligibility analysis (EUR 10k OSS threshold, EUR 150 IOSS per-consignment); (5) transfer_pricing_benchmark — OECD/JTPF operating-margin benchmarks by industry and country (20+ sectors, country-specific adjustments). Returns P0/P1/P2 compliance signals: P0=invalid VAT used for zero-rating, P1=taxable digital goods detected/audit risk, P2=filing deadlines/nexus alerts. Keyless — no API key required. Optional env: HMRC_VAT_API_KEY for UK VAT live validation. Cache TTL 24h.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "country": { "description": "ISO 3166-1 alpha-2 country code. Required for gst when query is ambiguous. Used in transfer_pricing_benchmark for country-specific OECD adjustments.", "type": "string" }, "mode": { "description": "Tax mode to invoke.", "enum": [ "vat_lookup", "sales_tax", "gst", "oss_ioss_eligibility", "transfer_pricing_benchmark" ], "type": "string" }, "query": { "description": "Mode-specific query: vat_lookup -> VAT number with country prefix (e.g. 'FR40303265045'); sales_tax -> US state code or name (e.g. 'CA', 'California'); gst -> ISO country code (e.g. 'SG', 'IN', 'AU'); oss_ioss_eligibility -> annual EU B2C revenue in EUR or keyword (e.g. '5000', 'below'); transfer_pricing_benchmark -> industry name (e.g. 'manufacturing', 'saas', 'r&d').", "type": "string" }, "transaction_type": { "description": "Transaction type for signal generation. 'digital' triggers GST/sales-tax digital goods warnings.", "enum": [ "B2B", "B2C", "digital", "physical" ], "type": "string" } }, "required": [ "mode", "query" ], "type": "object" }, "name": "tax_compliance_multi", "outputSchema": { "properties": { "gst": { "properties": { "country": { "type": "string" }, "country_code": { "type": "string" }, "digital_goods_taxable": { "type": "boolean" }, "reduced_rates": { "items": { "properties": { "applies_to": { "items": { "type": "string" }, "type": "array" }, "rate": { "type": "number" } }, "required": [ "rate", "applies_to" ], "type": "object" }, "type": "array" }, "registration_threshold_local": { "type": "number" }, "scheme_name": { "type": "string" }, "standard_rate_pct": { "type": "number" } }, "required": [ "country", "country_code", "standard_rate_pct", "digital_goods_taxable", "scheme_name" ], "type": "object" }, "mode": { "type": "string" }, "oss_ioss": { "properties": { "eligible_ioss": { "type": "boolean" }, "eligible_oss": { "type": "boolean" }, "next_reporting_deadline": { "type": "string" }, "requirements": { "items": { "type": "string" }, "type": "array" }, "threshold_eur": { "type": "number" } }, "required": [ "eligible_oss", "eligible_ioss", "threshold_eur", "requirements" ], "type": "object" }, "quality_score": { "type": "number" }, "sales_tax": { "properties": { "combined_rate_pct": { "type": "number" }, "digital_goods_taxable": { "type": "boolean" }, "local_rate_pct": { "type": "number" }, "nexus_threshold_usd": { "type": "number" }, "state": { "type": "string" }, "state_code": { "type": "string" }, "state_rate_pct": { "type": "number" } }, "required": [ "state", "state_code", "state_rate_pct", "combined_rate_pct", "digital_goods_taxable" ], "type": "object" }, "signals": { "items": { "properties": { "category": { "type": "string" }, "message": { "type": "string" }, "severity": { "enum": [ "P0", "P1", "P2" ], "type": "string" } }, "required": [ "severity", "category", "message" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "unavailable", "partial" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "transfer_pricing": { "properties": { "benchmark_margin_pct": { "type": "number" }, "country": { "type": "string" }, "industry": { "type": "string" }, "last_update": { "type": "string" }, "range_high_pct": { "type": "number" }, "range_low_pct": { "type": "number" }, "sample_size": { "type": "number" }, "source": { "enum": [ "OECD", "EU_JTPF", "private" ], "type": "string" } }, "required": [ "industry", "country", "benchmark_margin_pct", "range_low_pct", "range_high_pct", "sample_size", "last_update", "source" ], "type": "object" }, "vat_lookup": { "properties": { "address": { "type": "string" }, "country_code": { "type": "string" }, "last_checked": { "type": "string" }, "name": { "type": "string" }, "valid": { "type": "boolean" }, "vat_number": { "type": "string" }, "via": { "enum": [ "VIES", "HMRC", "format_check" ], "type": "string" } }, "required": [ "vat_number", "valid", "country_code", "last_checked", "via" ], "type": "object" } }, "required": [ "mode", "signals", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Optimisation fiscale — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Reference case: Pennylane — Fiscalité optimisée · CIR €1.2M · IP Box France 10% · Économie totale €2.4M/an. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "activities": { "items": { "maxLength": 200, "minLength": 5, "type": "string" }, "maxItems": 6, "minItems": 1, "type": "array" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "legalForm": { "maxLength": 100, "type": "string" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" }, "stage": { "enum": [ "pre-seed", "seed", "serie-a", "serie-b", "serie-c", "serie-d-plus", "profitable-sme", "enterprise" ], "type": "string" } }, "required": [ "name", "sector", "stage" ], "type": "object" }, "currentTaxOptimizations": { "items": { "maxLength": 200, "minLength": 2, "type": "string" }, "maxItems": 5, "minItems": 0, "type": "array" }, "financials": { "properties": { "annualRevenueEur": { "minimum": 0, "type": "number" }, "currentTaxRateEffectivePct": { "maximum": 100, "minimum": 0, "type": "number" }, "ebitdaEur": { "type": "number" }, "payrollEur": { "minimum": 0, "type": "number" }, "rdSpendEur": { "minimum": 0, "type": "number" } }, "required": [ "annualRevenueEur", "ebitdaEur" ], "type": "object" }, "ipAssets": { "items": { "maxLength": 200, "type": "string" }, "maxItems": 5, "minItems": 0, "type": "array" }, "jurisdictions": { "items": { "maxLength": 100, "minLength": 2, "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" } }, "required": [ "company", "financials", "jurisdictions", "activities" ], "type": "object" }, "name": "tax_optimization", "outputSchema": null }, { "description": "Négociation term sheet — Gapup agent-payable C-suite expertise (FUNDRAISING). Returns a structured, audited deliverable. Reference case: Agicap Série C €50M — 8 clauses analysées · 3 rouges · Score fondateur 62/100 → plan pour 81. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "round": { "properties": { "amountMEur": { "minimum": 0, "type": "number" }, "leadInvestor": { "maxLength": 100, "minLength": 2, "type": "string" }, "preMoneyValuation": { "minimum": 0, "type": "number" }, "stage": { "maxLength": 50, "minLength": 2, "type": "string" } }, "required": [ "stage", "amountMEur", "leadInvestor" ], "type": "object" }, "termSheetClauses": { "items": { "properties": { "clause": { "maxLength": 100, "minLength": 2, "type": "string" }, "currentText": { "maxLength": 500, "minLength": 5, "type": "string" } }, "required": [ "clause", "currentText" ], "type": "object" }, "maxItems": 15, "minItems": 3, "type": "array" } }, "required": [ "company", "round", "termSheetClauses" ], "type": "object" }, "name": "term_sheet_negotiation", "outputSchema": null }, { "description": "Cross-tool recommendation system: given a free-text intent, returns the most appropriate tools from the 170+ Gapup MCP catalogue, ranked by confidence, with pre-filled input suggestions and an optimal multi-tool chain when applicable. Use this first when you are unsure which tool to call — it navigates the full catalogue for you. Supports 15+ static pre-designed chains for frequent intents (M&A due diligence, sanctions screening, ESG 360, AI Act compliance, FTO patent clearance, crypto wallet tracking, etc.). Domains: compliance | finance | intel | legal | content | data | trade | infra. Pure compute — $0.01/call, no external fetch. Ideal as a first call in any multi-step agent workflow.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "domain": { "description": "Optional domain hint to boost tools in this category.", "enum": [ "compliance", "finance", "intel", "legal", "content", "data", "trade", "infra" ], "type": "string" }, "include_chain": { "description": "Whether to include a suggested_chain of tools in the optimal sequence. Default true. Chain is always included for well-known intents (M&A, compliance, ESG, etc.).", "type": "boolean" }, "intent": { "description": "Free-text description of what you want to accomplish. E.g. 'Run a full M&A due diligence on Acme Corp' or 'Je veux vérifier qu'un fournisseur n'est pas sous sanctions OFAC'. FR/EN/DE/ZH supported.", "type": "string" }, "lang": { "description": "Optional ISO 639-1 language hint (fr, en, de, zh, es …). Used for language-aware boosting.", "type": "string" }, "max_results": { "description": "Max number of recommendations returned (1-10). Default 5.", "type": "number" } }, "required": [ "intent" ], "type": "object" }, "name": "tool_recommend", "outputSchema": { "properties": { "alternative_paths": { "items": { "type": "string" }, "type": "array" }, "intent": { "type": "string" }, "not_covered": { "type": "string" }, "quality_score": { "type": "number" }, "recommendations": { "type": "array" }, "sources": { "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "suggested_chain": { "type": "array" } }, "required": [ "intent", "recommendations", "quality_score", "status" ], "type": "object" } }, { "description": "Evaluates trade finance eligibility for CFOs by analyzing counterparty risk and jurisdiction using World Bank and BIS data. Inputs include counterparty country code (ISO 3166-1 alpha-3) and industry sector. Returns risk scores, eligibility flags, and financing terms. Ideal for assessing letters of credit, export credit agency guarantees, and other trade finance instruments. Keywords: trade finance, counterparty risk, jurisdiction risk, letters of credit, ECA guarantees.", "inputSchema": { "properties": { "annualTradeVolumeUSD": { "minimum": 100000, "type": "number" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "counterpartyCountryCode": { "pattern": "^[A-Z]{3}$", "type": "string" }, "counterpartyCreditRating": { "enum": [ "AAA", "AA", "A", "BBB", "BB", "B", "CCC", "CC", "C", "D" ], "type": "string" }, "industrySector": { "enum": [ "manufacturing", "agriculture", "energy", "mining", "services", "technology" ], "type": "string" } }, "required": [ "counterpartyCountryCode", "industrySector" ], "type": "object" }, "name": "trade_finance_eligibility", "outputSchema": { "properties": { "countryRiskScore": { "maximum": 100, "minimum": 0, "type": "number" }, "eligibility": { "type": "boolean" }, "financingTerms": { "properties": { "interestRateBasisPoints": { "minimum": 0, "type": "number" }, "tenorMonths": { "minimum": 1, "type": "number" } }, "type": "object" }, "maxFinancingAmountUSD": { "minimum": 0, "type": "number" }, "recommendedInstruments": { "items": { "enum": [ "letter_of_credit", "export_credit_guarantee", "bank_guarantee", "supply_chain_finance" ], "type": "string" }, "type": "array" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Transcription and chapterization of long-form media (YouTube, podcasts, direct audio/video) for content marketing teams, podcast publishers, edu tech, journalists and accessibility/compliance.\n\nPipeline:\n• YouTube → timedtext captions (keyless) + oEmbed metadata + native timecode chapters from description\n• Podcast RSS → episode description + duration + timecodes if embedded in show notes\n• Direct media → partial (requires Whisper API via OPENAI_API_KEY + force_whisper:true)\n• Chapters: native YouTube timecodes preferred; heuristic TF-IDF segmentation as fallback\n• Summary: extractive TF-IDF top-sentences (no LLM required)\n• Language detection: character-set heuristic (CJK→zh, kana→ja, hangul→ko, accents→fr/de/es)\n\nOutput formats: json (full structured object) | text (plain transcript) | srt | vtt\n\nSLA: ≤15s budget total. Cache: 24h TTL.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "chapters_max": { "description": "Maximum number of chapters. Default 8.", "maximum": 30, "minimum": 1, "type": "number" }, "include_summary": { "description": "Include extractive summary. Default true.", "type": "boolean" }, "lang": { "description": "ISO 639-1 language hint (e.g. \"en\", \"fr\", \"de\"). Default \"auto\".", "maxLength": 10, "type": "string" }, "output_format": { "description": "Transcript format. Default \"json\".", "enum": [ "text", "srt", "vtt", "json" ], "type": "string" }, "url": { "description": "YouTube URL, podcast RSS feed URL, or direct MP3/MP4 URL. Example: \"https://www.youtube.com/watch?v=jNQXAC9IVRw\"", "type": "string" } }, "required": [ "url" ], "type": "object" }, "name": "transcribe_chapterize_media", "outputSchema": { "properties": { "chapters": { "items": { "properties": { "start_s": { "type": "number" }, "summary": { "type": "string" }, "title": { "type": "string" } }, "required": [ "start_s", "title", "summary" ], "type": "object" }, "type": "array" }, "duration_seconds": { "type": "number" }, "key_topics": { "items": { "type": "string" }, "type": "array" }, "lang_detected": { "type": "string" }, "quality_score": { "type": "number" }, "segments": { "items": { "properties": { "end_s": { "type": "number" }, "speaker": { "type": "string" }, "start_s": { "type": "number" }, "text": { "type": "string" } }, "required": [ "start_s", "end_s", "text" ], "type": "object" }, "type": "array" }, "signals": { "items": { "properties": { "category": { "type": "string" }, "message": { "type": "string" }, "severity": { "enum": [ "P0", "P1", "P2" ], "type": "string" } }, "required": [ "severity", "category", "message" ], "type": "object" }, "type": "array" }, "source_type": { "enum": [ "youtube", "podcast", "direct_media", "unknown" ], "type": "string" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "partial", "unavailable" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "summary": { "type": "string" }, "transcript": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "source_type", "duration_seconds", "lang_detected", "transcript", "segments", "chapters", "key_topics", "signals", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Optimiseur de trésorerie — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Reference case: Alan — Trésorerie €380M post-Série F · Allocation optimale 4 instruments · Yield +145bp · +€5.5M/an. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "cashPosition": { "properties": { "currencies": { "items": { "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" }, "monthlyBurnEur": { "minimum": 0, "type": "number" }, "operationalReserveMonths": { "maximum": 24, "minimum": 1, "type": "number" }, "totalCashEur": { "minimum": 0, "type": "number" } }, "required": [ "totalCashEur", "operationalReserveMonths", "monthlyBurnEur", "currencies" ], "type": "object" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 120, "minLength": 2, "type": "string" }, "stage": { "maxLength": 50, "type": "string" } }, "required": [ "name", "sector", "stage" ], "type": "object" }, "constraints": { "properties": { "bankPartners": { "items": { "type": "string" }, "maxItems": 5, "minItems": 0, "type": "array" }, "maxLockupDays": { "maximum": 365, "minimum": 0, "type": "integer" }, "riskAppetite": { "enum": [ "very-conservative", "conservative", "moderate" ], "type": "string" } }, "required": [ "bankPartners" ], "type": "object" }, "horizon": { "enum": [ "3mo", "6mo", "12mo" ], "type": "string" } }, "required": [ "company", "cashPosition", "constraints" ], "type": "object" }, "name": "treasury_optimizer", "outputSchema": null }, { "description": "Monitor emerging trends, regulatory shifts and adoption signals for a given market sector. Returns 5-12 trend cards, each with a momentum score (rising/stable/declining), a 3-month and 12-month outlook, opportunity windows, and recommended actions. When to use this tool: the user asks what is heating up in a market, wants to time a product roadmap or content calendar, or needs an early read on a sector. Inputs: a sector to monitor and 3-8 keywords defining the watch perimeter. Delivered by Manue, the AI CMO of the Gapup portfolio.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "focus": { "description": "Optional context (geography, language target, comparator window, etc.)", "maxLength": 400, "type": "string" }, "keywords": { "description": "3-8 keywords describing the watch perimeter", "items": { "maxLength": 60, "minLength": 2, "type": "string" }, "maxItems": 8, "minItems": 3, "type": "array" }, "sector": { "description": "Sector to monitor (e.g. 'B2B SaaS productivity', 'EU fintech', 'climate-tech hardware')", "maxLength": 160, "minLength": 2, "type": "string" } }, "required": [ "sector", "keywords" ], "type": "object" }, "name": "trend_watcher", "outputSchema": { "properties": { "executiveSummary": { "description": "Board-ready sector overview prose", "type": "string" }, "kpis": { "description": "3-5 headline KPI bubbles", "items": { "properties": { "label": { "type": "string" }, "trend": { "enum": [ "up", "down", "stable" ], "type": "string" }, "value": { "type": "string" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "recommendations": { "description": "Prioritised strategic recommendations", "items": { "properties": { "action": { "type": "string" }, "horizon": { "type": "string" }, "rationale": { "type": "string" } }, "required": [ "action", "rationale" ], "type": "object" }, "type": "array" }, "trends": { "description": "5-12 trend cards for the sector", "items": { "properties": { "description": { "type": "string" }, "momentum": { "enum": [ "rising", "stable", "declining" ], "type": "string" }, "opportunityWindows": { "items": { "type": "string" }, "type": "array" }, "outlook12mo": { "type": "string" }, "outlook3mo": { "type": "string" }, "recommendedActions": { "items": { "type": "string" }, "type": "array" }, "sources": { "items": { "properties": { "excerpt": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url" ], "type": "object" }, "type": "array" }, "title": { "type": "string" } }, "required": [ "title", "momentum", "description", "outlook3mo", "outlook12mo" ], "type": "object" }, "type": "array" } }, "required": [ "executiveSummary", "trends" ], "type": "object" } }, { "description": "Multi-language UGC content moderation for marketplaces, social platforms and comment systems. Detects policy violations in text content across 9 policies and 12 languages without external API calls.\n\nPolicies checked:\n• hate — hate speech, slurs, dehumanization (50+ terms × 12 languages)\n• sexual — explicit sexual content, pornography references, nudity solicitation\n• violence — threats, weapon references, graphic violence\n• self_harm — suicidal ideation, self-injury, eating disorder promotion\n• harassment — doxxing, stalking, cyberbullying, blackmail\n• scam — phishing, investment fraud, romance scam, lottery fraud\n• spam — bots, keyword stuffing, excessive caps, emoji storms, suspicious URLs\n• copyright — piracy, leaked content, serial keys, streaming fraud\n• minor_safety — grooming signals, CSAM references, minor + adult content combos\n\nLanguages: en / fr / de / es / it / pt / nl / zh / ja / ko / ar / ru (auto-detected)\n\nOutput includes severity (low/medium/high/severe), confidence (0-100), matched patterns, excerpt, recommended action, age appropriateness (adult/teen/child), and signals.\n\nNo API key required. Stateless — no content is stored or logged.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "content": { "description": "Text content to moderate (comment, review, post, chat message).", "maxLength": 10000, "minLength": 1, "type": "string" }, "content_type": { "description": "Type of content. Affects recommended_action heuristic. Default: comment.", "enum": [ "comment", "review", "post", "chat" ], "type": "string" }, "lang": { "description": "Language override. If omitted, language is auto-detected.", "enum": [ "en", "fr", "de", "es", "it", "pt", "nl", "zh", "ja", "ko", "ar", "ru" ], "type": "string" }, "policies": { "description": "Policies to check. Default: all 9 policies.", "items": { "enum": [ "hate", "sexual", "violence", "self_harm", "harassment", "scam", "spam", "copyright", "minor_safety" ], "type": "string" }, "type": "array" } }, "required": [ "content" ], "type": "object" }, "name": "ugc_moderation_classifier", "outputSchema": { "properties": { "age_appropriate": { "properties": { "adult": { "type": "boolean" }, "child": { "type": "boolean" }, "teen": { "type": "boolean" } }, "required": [ "adult", "teen", "child" ], "type": "object" }, "content_preview": { "type": "string" }, "lang_detected": { "type": "string" }, "policies_checked": { "items": { "type": "string" }, "type": "array" }, "quality_score": { "type": "number" }, "recommended_action": { "enum": [ "allow", "flag_for_review", "shadow_ban", "remove", "escalate" ], "type": "string" }, "signals": { "items": { "properties": { "category": { "type": "string" }, "message": { "type": "string" }, "severity": { "enum": [ "info", "warning", "critical" ], "type": "string" } }, "required": [ "severity", "category", "message" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "violations": { "items": { "properties": { "confidence": { "type": "number" }, "excerpt": { "type": "string" }, "matched_patterns": { "items": { "type": "string" }, "type": "array" }, "policy": { "type": "string" }, "severity": { "enum": [ "low", "medium", "high", "severe" ], "type": "string" } }, "required": [ "policy", "severity", "confidence", "matched_patterns", "excerpt" ], "type": "object" }, "type": "array" } }, "required": [ "content_preview", "lang_detected", "policies_checked", "violations", "recommended_action", "age_appropriate", "signals", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Chasseur d'upsell — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Gapup Hub — Upsell 8 comptes · €127k potentiel · Top 3 : Alan+Qonto+Pennylane · Playbook 5 étapes. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "accounts": { "items": { "properties": { "activeUsers": { "minimum": 0, "type": "number" }, "contractRenewalDays": { "minimum": 0, "type": "number" }, "currentArrEur": { "minimum": 0, "type": "number" }, "id": { "maxLength": 50, "minLength": 1, "type": "string" }, "lastEngagementDays": { "minimum": 0, "type": "number" }, "maxContractUsers": { "minimum": 0, "type": "number" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "npsScore": { "maximum": 10, "minimum": 0, "type": "number" }, "signals": { "items": { "maxLength": 200, "type": "string" }, "maxItems": 5, "minItems": 0, "type": "array" }, "tier": { "enum": [ "strategic", "enterprise", "mid-market", "smb" ], "type": "string" }, "usageRatePct": { "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "id", "name", "currentArrEur", "tier" ], "type": "object" }, "maxItems": 20, "minItems": 2, "type": "array" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "horizon": { "enum": [ "30d", "90d", "180d" ], "type": "string" }, "product": { "properties": { "availableUpsells": { "items": { "maxLength": 200, "type": "string" }, "maxItems": 8, "minItems": 1, "type": "array" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" } }, "required": [ "name", "availableUpsells" ], "type": "object" }, "targetUpsellEur": { "minimum": 0, "type": "number" } }, "required": [ "company", "product", "accounts" ], "type": "object" }, "name": "upsell_hunter", "outputSchema": null }, { "description": "Real-time analytics on x402 protocol USDC micropayments for MCP endpoints on Base network. Unique competitive advantage: aggregates internal production telemetry (our own traffic data) with on-chain USDC Transfer events and Bazaar marketplace listings — data no external competitor can access. Four modes: (1) facilitator_stats — Coinbase x402 facilitator settlement statistics (volume, count, top payees/payers). Uses Coinbase CDP API if COINBASE_X402_API_KEY is set; falls back to Base mainnet RPC scan of USDC transfers to known facilitator addresses. (2) endpoint_intel — Per-MCP-endpoint analytics: tx count, USDC volume, unique callers, success rate, catalog size. For gapup-mcp.io endpoints: reads internal JSONL telemetry (richest data source, unique). (3) agent_caller_profile — Anonymous profile of a calling agent wallet: tx count, USDC spent, top endpoints, inferred persona (depth-seeker / bulk-scanner / generalist / researcher / explorer). Wallet anonymised via SHA-256. (4) price_radar — USDC price distribution by tool category (data_lookup / synthesis / compliance / competitive) from Bazaar + internal catalog. Returns median, P25, P75. Network: Base mainnet. USDC contract: 0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913. Cache: 30 min LRU. Timeout per source: 8s. Optional env: COINBASE_X402_API_KEY (higher-fidelity facilitator stats).", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "category": { "description": "Tool category for price_radar mode. Defaults to all.", "enum": [ "data_lookup", "synthesis", "compliance", "competitive", "all" ], "type": "string" }, "endpoint_url": { "description": "MCP endpoint URL for endpoint_intel mode (e.g. https://mcp.gapup.io/mcp)", "type": "string" }, "mode": { "description": "Analytics mode: facilitator_stats=network-wide settlements | endpoint_intel=per-URL analytics | agent_caller_profile=per-wallet analytics | price_radar=price distribution by category", "enum": [ "facilitator_stats", "endpoint_intel", "agent_caller_profile", "price_radar" ], "type": "string" }, "period_days": { "description": "Lookback window in days (5-90, default 30)", "maximum": 90, "minimum": 5, "type": "number" }, "wallet_address": { "description": "EVM wallet address for agent_caller_profile mode (0x...)", "type": "string" } }, "required": [ "mode" ], "type": "object" }, "name": "usdc_x402_payments_intel", "outputSchema": { "properties": { "agent_caller_profile": { "type": "object" }, "endpoint_intel": { "type": "object" }, "facilitator_stats": { "type": "object" }, "mode": { "type": "string" }, "price_radar": { "items": { "type": "object" }, "type": "array" }, "quality_score": { "type": "number" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "unavailable", "partial" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" } }, "required": [ "mode", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "As a COO, quickly check if a vendor is blacklisted for ESG non-compliance using CDP and GRI data. Input the vendor's legal name or identifier to receive their ESG risk score, blacklist status, and compliance violations. Returns structured data including CDP disclosure score, GRI alignment, and any regulatory flags. Ideal for vendor due diligence, risk assessment, and sustainability reporting. Keywords: ESG, vendor risk, compliance, CDP, GRI, sustainability, blacklist.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "vendorId": { "description": "Optional identifier (e.g., LEI, DUNS)", "type": "string" }, "vendorName": { "description": "Legal name of the vendor to check", "type": "string" }, "year": { "default": 2026, "description": "Reporting year (default: current year)", "type": "number" } }, "required": [ "vendorName" ], "type": "object" }, "name": "vendor_esg_blacklist_monitor", "outputSchema": { "properties": { "blacklisted": { "type": "boolean" }, "cdpDisclosureScore": { "enum": [ "A", "B", "C", "D", "E", "F", "Not Disclosed" ], "type": "string" }, "esgRiskScore": { "maximum": 100, "minimum": 0, "type": "number" }, "griAligned": { "type": "boolean" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "vendorId": { "type": "string" }, "vendorName": { "type": "string" }, "violations": { "items": { "properties": { "description": { "type": "string" }, "severity": { "enum": [ "low", "medium", "high" ], "type": "string" }, "type": { "type": "string" } }, "required": [ "type", "description", "severity" ], "type": "object" }, "type": "array" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status", "vendorName", "blacklisted", "warnings", "sources" ], "type": "object" } }, { "description": "For COOs: scans vendor ESG reports to identify suppliers lacking diversity disclosures in GRI or CDP filings. Input a supplier name or identifier to receive a structured assessment of gender, ethnicity, and board diversity metrics. Returns compliance gaps, missing data flags, and source references from CDP open data and GRI standards. Ideal for vendor risk assessment and ESG compliance tracking.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "supplierId": { "description": "CDP or GRI identifier for the supplier (e.g., CDP company ID)", "type": "string" }, "supplierName": { "description": "Exact or partial name of the supplier to scan", "type": "string" }, "year": { "description": "Reporting year to check (default: current year)", "type": "number" } }, "required": [ "supplierName" ], "type": "object" }, "name": "vendor_esg_diversity_scanner", "outputSchema": { "properties": { "complianceScore": { "description": "Percentage compliance with diversity disclosure standards", "maximum": 100, "minimum": 0, "type": "number" }, "diversityDisclosures": { "properties": { "boardDiversity": { "description": "True if board diversity metrics are disclosed", "type": "boolean" }, "ethnicityDiversity": { "description": "True if ethnicity diversity metrics are disclosed", "type": "boolean" }, "genderDiversity": { "description": "True if gender diversity metrics are disclosed", "type": "boolean" }, "missingMetrics": { "description": "List of missing diversity metrics", "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "reportLinks": { "description": "URLs to relevant ESG reports", "items": { "type": "string" }, "type": "array" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "supplierName": { "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status", "supplierName", "diversityDisclosures", "complianceScore" ], "type": "object" } }, { "description": "Gestion des fournisseurs — Gapup agent-payable C-suite expertise (COO). Returns a structured, audited deliverable. Reference case: Qonto (12 fournisseurs · €2.4M/an) — €290k économies identifiées · 4 renegociations prioritaires. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" }, "totalVendorSpendEur": { "minimum": 0, "type": "number" } }, "required": [ "name", "sector", "totalVendorSpendEur" ], "type": "object" }, "objectives": { "properties": { "complianceFocus": { "type": "boolean" }, "consolidationTarget": { "type": "boolean" }, "targetSavingsPct": { "maximum": 50, "minimum": 0, "type": "number" } }, "type": "object" }, "vendors": { "items": { "properties": { "annualSpendEur": { "minimum": 0, "type": "number" }, "category": { "maxLength": 60, "minLength": 2, "type": "string" }, "contractEndDate": { "maxLength": 20, "type": "string" }, "criticalityLevel": { "enum": [ "critical", "important", "standard" ], "type": "string" }, "name": { "maxLength": 100, "minLength": 2, "type": "string" }, "satisfactionScore": { "maximum": 10, "minimum": 0, "type": "number" }, "singleSource": { "type": "boolean" } }, "required": [ "name", "category", "annualSpendEur", "criticalityLevel" ], "type": "object" }, "maxItems": 20, "minItems": 3, "type": "array" } }, "required": [ "company", "vendors", "objectives" ], "type": "object" }, "name": "vendor_management", "outputSchema": null }, { "description": "Évaluateur de risque fournisseurs — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Reference case: Gapup Hub — 15 fournisseurs · €1.8M spend · 3 critiques · Heatmap + plan de remédiation. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "assessmentPurpose": { "enum": [ "annual-review", "due-diligence", "audit", "board-report" ], "type": "string" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "isRegulated": { "type": "boolean" }, "name": { "maxLength": 100, "type": "string" }, "sector": { "maxLength": 100, "type": "string" } }, "required": [ "name", "sector" ], "type": "object" }, "riskFramework": { "items": { "enum": [ "cyber", "financial", "operational", "regulatory", "esg", "concentration" ], "type": "string" }, "type": "array" }, "vendors": { "items": { "properties": { "annualSpendEur": { "minimum": 0, "type": "number" }, "category": { "maxLength": 100, "type": "string" }, "contractExpiryMonths": { "minimum": 0, "type": "integer" }, "criticality": { "enum": [ "critical", "important", "standard", "low" ], "type": "string" }, "dataAccess": { "type": "boolean" }, "id": { "maxLength": 20, "type": "string" }, "iso27001": { "type": "boolean" }, "knownIssues": { "items": { "maxLength": 200, "type": "string" }, "maxItems": 5, "type": "array" }, "name": { "maxLength": 100, "type": "string" }, "soc2": { "type": "boolean" } }, "required": [ "id", "name", "category" ], "type": "object" }, "maxItems": 50, "minItems": 3, "type": "array" } }, "required": [ "company", "vendors" ], "type": "object" }, "name": "vendor_risk_assessor", "outputSchema": null }, { "description": "Generates a comprehensive vertical AI agent workforce integration plan for CHROs, including governance frameworks, human-AI collaboration metrics, and upskilling recommendations. Inputs: industry vertical, workforce size, and current AI adoption level. Outputs: role-specific AI integration roadmaps, skill gap analysis, and performance benchmarks. Uses O*NET skill taxonomies and Gartner AI adoption trends. For best results with large datasets, pass async:true to avoid timeout.", "inputSchema": { "properties": { "ai_adoption_level": { "enum": [ "exploratory", "pilot", "scaling", "mature" ], "type": "string" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "include_benchmarks": { "default": true, "type": "boolean" }, "industry": { "enum": [ "healthcare", "finance", "manufacturing", "retail", "technology", "education" ], "type": "string" }, "target_roles": { "items": { "type": "string" }, "type": "array" }, "workforce_size": { "maximum": 500000, "minimum": 10, "type": "number" } }, "required": [ "industry", "workforce_size" ], "type": "object" }, "name": "vertical_ai_agent_governance", "outputSchema": { "properties": { "collaboration_metrics": { "properties": { "error_reduction_rate": { "type": "number" }, "human_ai_productivity_ratio": { "type": "number" }, "task_automation_rate": { "type": "number" } }, "type": "object" }, "governance_recommendations": { "items": { "properties": { "category": { "type": "string" }, "implementation_complexity": { "enum": [ "low", "medium", "high" ], "type": "string" }, "recommendation": { "type": "string" } }, "type": "object" }, "type": "array" }, "integration_roadmap": { "properties": { "phases": { "items": { "properties": { "key_actions": { "items": { "type": "string" }, "type": "array" }, "metrics": { "items": { "type": "string" }, "type": "array" }, "phase": { "type": "string" }, "timeline_months": { "type": "number" } }, "type": "object" }, "type": "array" } }, "type": "object" }, "skill_gap_analysis": { "properties": { "current_skills": { "items": { "type": "string" }, "type": "array" }, "gap_severity": { "enum": [ "low", "medium", "high", "critical" ], "type": "string" }, "required_skills": { "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "sources": { "items": { "properties": { "fetched_at": { "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "As a CTO, assess the exploitability risk of CVEs using EPSS scores and cloud asset exposure data. Input a CVE ID (e.g., CVE-2021-44228) to receive exploitability likelihood, affected cloud services, and threat intelligence context. Returns structured risk metrics for prioritization. Sources: CVE NVD, OpenCVE, GitHub Advisories. Pass async:true to avoid timeout.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "cloudProvider": { "enum": [ "aws", "gcp", "azure", "" ], "type": "string" }, "cveId": { "pattern": "^CVE-\\d{4}-\\d{4,}$", "type": "string" }, "includeDetails": { "default": false, "type": "boolean" } }, "required": [ "cveId" ], "type": "object" }, "name": "vuln_exploitability_forecast", "outputSchema": { "properties": { "cloudExposure": { "properties": { "affectedServices": { "items": { "type": "string" }, "type": "array" }, "exposureLevel": { "enum": [ "none", "low", "medium", "high", "critical" ], "type": "string" } }, "type": "object" }, "cveId": { "type": "string" }, "epssPercentile": { "maximum": 100, "minimum": 0, "type": "number" }, "epssScore": { "maximum": 1, "minimum": 0, "type": "number" }, "lastUpdated": { "format": "date-time", "type": "string" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "source": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at", "source" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status", "cveId", "warnings", "sources" ], "type": "object" } }, { "description": "As a CTO, quickly prioritize unpatched CVEs by combining exploitability scores (EPSS) with cloud asset criticality. Input a list of CVE IDs and your AWS service types (e.g., EC2, RDS) to receive a ranked patching order with risk scores and estimated cloud impact. Uses public NVD, OpenCVE, and AWS pricing data. Ideal for vulnerability management and cloud security posture improvement.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "awsServices": { "description": "AWS service types affected by these CVEs (e.g., [\"EC2\", \"RDS\", \"Lambda\"])", "items": { "type": "string" }, "type": "array" }, "cveIds": { "description": "List of CVE identifiers to analyze (e.g., [\"CVE-2021-44228\", \"CVE-2023-3824\"])", "items": { "type": "string" }, "type": "array" }, "maxResults": { "default": 10, "description": "Maximum number of prioritized CVEs to return (default: 10)", "type": "number" } }, "required": [ "cveIds" ], "type": "object" }, "name": "vuln_patch_priority_engine", "outputSchema": { "properties": { "prioritizedCves": { "items": { "properties": { "affectedAwsServices": { "items": { "type": "string" }, "type": "array" }, "cloudImpactScore": { "type": "number" }, "cveId": { "type": "string" }, "cvssScore": { "type": "number" }, "epssScore": { "type": "number" }, "patchRecommendation": { "type": "string" }, "priorityScore": { "type": "number" } }, "required": [ "cveId", "priorityScore" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "fetched_at": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Physical climate intelligence for insurance underwriting, agritech, logistics, energy trading and ESG/climate risk disclosure. Three modes: (1) forecast — 14-day daily weather forecast with temperature, precipitation, wind and humidity; (2) historical — daily records and monthly aggregates for any date range since 1940, with anomaly detection (P90/P95 heat events, extreme precipitation days); (3) climate_risk — long-term physical risk scoring combining CMIP6 ensemble projections (2020-2050), altitude, FEMA flood zones (US) and historical baselines. Risk dimensions: flood, heat (days >35°C/year), drought (SPI), wildfire, sea-level. Overall score 0-100 (100 = severe). Location: city string or lat/lon coordinates. Sources: Open-Meteo (keyless, global, 1940→2050), Open-Elevation, FEMA NFHL (US), NOAA CDO (optional NOAA_API_KEY env var for US+global station data). SLA: ≤25s p95. Cache: 1h forecast / 24h historical / 7d climate_risk.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "date_from": { "description": "ISO date YYYY-MM-DD — start of date range (required for historical/climate_risk)", "type": "string" }, "date_to": { "description": "ISO date YYYY-MM-DD — end of date range (required for historical/climate_risk)", "type": "string" }, "location": { "description": "Geographic location. Provide either {city, country?} or {lat, lon}.", "properties": { "city": { "description": "City name (e.g. 'Paris', 'Miami', 'Tokyo')", "type": "string" }, "country": { "description": "Country name or ISO code (e.g. 'France', 'US', 'JP')", "type": "string" }, "lat": { "description": "Latitude in decimal degrees", "type": "number" }, "lon": { "description": "Longitude in decimal degrees", "type": "number" } }, "type": "object" }, "metrics": { "description": "Weather metrics to include. Default: all metrics.", "items": { "enum": [ "temperature", "precipitation", "wind", "humidity", "solar" ], "type": "string" }, "type": "array" }, "mode": { "description": "'forecast' (14 days), 'historical' (date range since 1940), 'climate_risk' (long-term physical risk score)", "enum": [ "forecast", "historical", "climate_risk" ], "type": "string" } }, "required": [ "location", "mode" ], "type": "object" }, "name": "weather_climate_intel", "outputSchema": { "properties": { "climate_risk": { "properties": { "drought_risk": { "properties": { "level": { "type": "string" }, "reasoning": { "type": "string" }, "spi_3mo": { "type": "number" } }, "required": [ "level", "reasoning" ], "type": "object" }, "flood_risk": { "properties": { "level": { "type": "string" }, "percentile": { "type": "number" }, "reasoning": { "type": "string" } }, "required": [ "level", "reasoning" ], "type": "object" }, "heat_risk": { "properties": { "days_over_35c_per_year": { "type": "number" }, "level": { "type": "string" }, "reasoning": { "type": "string" } }, "required": [ "level", "reasoning" ], "type": "object" }, "overall_score": { "type": "number" }, "sea_level_risk": { "properties": { "distance_to_coast_km": { "type": "number" }, "level": { "type": "string" }, "m_above_sea": { "type": "number" }, "reasoning": { "type": "string" } }, "required": [ "level", "reasoning" ], "type": "object" }, "wildfire_risk": { "properties": { "level": { "type": "string" }, "reasoning": { "type": "string" } }, "required": [ "level", "reasoning" ], "type": "object" } }, "required": [ "flood_risk", "heat_risk", "drought_risk", "overall_score" ], "type": "object" }, "forecast": { "properties": { "daily": { "items": { "properties": { "date": { "type": "string" }, "precip_mm": { "type": [ "number", "null" ] }, "summary": { "type": "string" }, "t_max": { "type": [ "number", "null" ] }, "t_min": { "type": [ "number", "null" ] }, "wind_max": { "type": [ "number", "null" ] } }, "required": [ "date", "t_min", "t_max", "precip_mm", "wind_max", "summary" ], "type": "object" }, "type": "array" } }, "required": [ "daily" ], "type": "object" }, "historical": { "properties": { "anomalies": { "items": { "type": "object" }, "type": "array" }, "daily": { "items": { "type": "object" }, "type": "array" }, "monthly_aggregates": { "items": { "properties": { "month": { "type": "string" }, "precip_total_mm": { "type": "number" }, "t_max_avg": { "type": "number" }, "t_min_avg": { "type": "number" }, "wind_max_avg": { "type": "number" } }, "required": [ "month", "t_min_avg", "t_max_avg", "precip_total_mm", "wind_max_avg" ], "type": "object" }, "type": "array" } }, "required": [ "daily", "monthly_aggregates", "anomalies" ], "type": "object" }, "location": { "properties": { "country": { "type": "string" }, "lat": { "type": "number" }, "lon": { "type": "number" }, "resolved_name": { "type": "string" }, "timezone": { "type": "string" } }, "required": [ "resolved_name", "lat", "lon" ], "type": "object" }, "mode": { "enum": [ "forecast", "historical", "climate_risk" ], "type": "string" }, "quality_score": { "type": "number" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "enum": [ "retrieved", "partial", "unavailable" ], "type": "string" }, "url": { "type": "string" } }, "required": [ "name", "url", "status" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" } }, "required": [ "location", "mode", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Get a direct, cited answer to a question, grounded in live web sources. Returns the answer text plus the sources it was built from. Use when you want a conclusion rather than a list of links.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "includeText": { "description": "Include the full text of each cited source (default false)", "type": "boolean" }, "query": { "description": "The question to answer", "maxLength": 500, "minLength": 3, "type": "string" } }, "required": [ "query" ], "type": "object" }, "name": "web_answer", "outputSchema": null }, { "description": "Extract the full text of specific URLs, with optional highlights and a summary. Use when you already know which pages you need, rather than searching for them.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "highlights": { "description": "Also return the most relevant excerpts", "type": "boolean" }, "summary": { "description": "Also return a short summary of each page", "type": "boolean" }, "urls": { "description": "The URLs to extract", "items": { "type": "string" }, "maxItems": 20, "minItems": 1, "type": "array" } }, "required": [ "urls" ], "type": "object" }, "name": "web_contents", "outputSchema": null }, { "description": "Neural and keyword web search over the live web. Returns ranked results with title, URL, publication date and author, optionally with the page text. Sub-second on repeat queries (5-minute cache).", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "category": { "description": "Restrict results to one kind of page", "enum": [ "company", "research paper", "news", "pdf", "github", "tweet", "personal site", "linkedin profile", "financial report" ], "type": "string" }, "excludeDomains": { "items": { "type": "string" }, "maxItems": 20, "type": "array" }, "includeDomains": { "items": { "type": "string" }, "maxItems": 20, "type": "array" }, "includeText": { "description": "Include the page text in each result (default false)", "type": "boolean" }, "numResults": { "description": "How many results (default 10)", "maximum": 25, "minimum": 1, "type": "integer" }, "query": { "description": "The search query", "maxLength": 500, "minLength": 2, "type": "string" }, "startPublishedDate": { "description": "ISO-8601 date; only pages published after it", "type": "string" }, "type": { "description": "Search mode (default auto)", "enum": [ "neural", "keyword", "auto" ], "type": "string" } }, "required": [ "query" ], "type": "object" }, "name": "web_search", "outputSchema": null }, { "description": "Multi-language, multi-source web search that goes beyond Anglo-centric results. Supports 15 languages (fr/de/es/it/pt/nl/ja/zh/ko/ar/ru/sv/pl/tr/en) with automatic detection. Aggregates results from Mojeek (independent search engine, multilang) and Wikipedia (native multilang API), with DDG and HN as English-language complements. Returns deduplicated results ranked by cross-engine consensus. Use when you need non-English search results, when DDG fails, or for geographically-biased queries. Phase 2 #7 of the geo/lang expansion plan. Note: Brave/Bing/Searx are blocked from DO IPs — configure AICI_RESEARCH_PROXY_URL for residential proxy.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "country": { "description": "ISO-3166-1 alpha-2 country code for geographic bias (e.g. FR, DE, JP, BR). Optional.", "pattern": "^[A-Z]{2}$", "type": "string" }, "lang": { "description": "2-letter language code. If omitted, auto-detected from query characters and lexical markers.", "pattern": "^(fr|de|es|it|pt|nl|ja|zh|ko|ar|ru|sv|pl|tr|en)$", "type": "string" }, "max_results": { "description": "Maximum number of results to return (default 10).", "maximum": 20, "minimum": 5, "type": "integer" }, "query": { "description": "Search query in any language", "maxLength": 400, "minLength": 2, "type": "string" } }, "required": [ "query" ], "type": "object" }, "name": "web_search_multilang", "outputSchema": { "properties": { "by_engine": { "additionalProperties": { "properties": { "count": { "type": "integer" }, "status": { "enum": [ "retrieved", "partial", "unavailable" ], "type": "string" } }, "type": "object" }, "type": "object" }, "country_used": { "type": [ "string", "null" ] }, "lang_used": { "type": "string" }, "quality_score": { "maximum": 100, "minimum": 0, "type": "integer" }, "query": { "type": "string" }, "results": { "items": { "properties": { "rank": { "type": "integer" }, "snippet": { "type": "string" }, "source_engine": { "enum": [ "mojeek", "wikipedia_fulltext", "ddg", "hn" ], "type": "string" }, "title": { "type": "string" }, "url": { "type": "string" } }, "required": [ "title", "url", "snippet", "source_engine", "rank" ], "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "name": { "type": "string" }, "status": { "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "status": { "enum": [ "partial", "final", "failed" ], "type": "string" }, "total_unique_results": { "type": "integer" } }, "required": [ "query", "lang_used", "results", "by_engine", "total_unique_results", "sources", "quality_score", "status" ], "type": "object" } }, { "description": "Manage HTTP webhook callbacks for async tools (T5/T6 batch flagships). Instead of polling every 5s, register a callback URL — Gapup posts the job result to your endpoint the moment it completes. Supported events: job.completed | job.failed | monitoring.alert | quota.threshold. Modes: register (add endpoint), list (view active webhooks), revoke (soft-delete), test (fire a test payload to verify your receiver), history (last 20 fires). Security: every delivery is signed with HMAC-SHA256 on the body — verify the X-Gapup-Signature header against sha256(secret, body).", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "caller_hash": { "description": "Optional caller identity override. If omitted, uses the internal session hash.", "type": "string" }, "events": { "description": "(register, optional) Events to subscribe to. Defaults to all events if omitted.", "items": { "enum": [ "job.completed", "job.failed", "monitoring.alert", "quota.threshold" ], "type": "string" }, "type": "array" }, "mode": { "description": "register — add a webhook endpoint. list — view your active webhooks. revoke — soft-delete a webhook by webhook_id. test — fire a test payload to verify the receiver is alive. history — last 20 delivery attempts for a webhook.", "enum": [ "register", "list", "revoke", "test", "history" ], "type": "string" }, "secret": { "description": "(register, optional) A secret string used to sign deliveries with HMAC-SHA256. Store it safely — verify X-Gapup-Signature header on your receiver.", "type": "string" }, "url": { "description": "(register) HTTPS/HTTP endpoint that will receive POST callbacks. Must return 2xx within 10s.", "type": "string" }, "webhook_id": { "description": "(revoke / test / history) The webhook_id returned from register.", "type": "string" } }, "required": [ "mode" ], "type": "object" }, "name": "webhooks_manage", "outputSchema": { "description": "Response varies by mode.", "type": "object" } }, { "description": "Analyse Win/Loss deals — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Gapup Hub — Win/Loss 32 deals Q1 2026 · Win rate 41% → 68% potentiel · Playbook 8 actions CRO. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" }, "stage": { "enum": [ "pre-seed", "seed", "serie-a", "serie-b", "serie-c", "serie-d-plus", "profitable-sme", "enterprise" ], "type": "string" } }, "required": [ "name", "sector", "stage" ], "type": "object" }, "deals": { "items": { "properties": { "competitor": { "maxLength": 100, "type": "string" }, "dealValueEur": { "minimum": 0, "type": "number" }, "durationDays": { "minimum": 1, "type": "number" }, "id": { "maxLength": 50, "minLength": 1, "type": "string" }, "lostReason": { "maxLength": 300, "type": "string" }, "notes": { "maxLength": 500, "type": "string" }, "outcome": { "enum": [ "won", "lost", "ghosted", "no-decision" ], "type": "string" } }, "required": [ "id", "outcome", "dealValueEur", "durationDays" ], "type": "object" }, "maxItems": 50, "minItems": 3, "type": "array" }, "primaryChallenge": { "maxLength": 400, "minLength": 10, "type": "string" }, "product": { "properties": { "avgContractValueEur": { "minimum": 0, "type": "number" }, "category": { "maxLength": 120, "minLength": 2, "type": "string" }, "name": { "maxLength": 200, "minLength": 2, "type": "string" } }, "required": [ "name", "category", "avgContractValueEur" ], "type": "object" }, "salesCycleTargetDays": { "maximum": 730, "minimum": 1, "type": "number" }, "topCompetitors": { "items": { "type": "string" }, "maxItems": 5, "minItems": 0, "type": "array" } }, "required": [ "company", "product", "deals" ], "type": "object" }, "name": "win_loss_decoder", "outputSchema": null }, { "description": "Meta-tool that CHAINS multiple MCP tools sequentially into a named workflow — delivering a composite output in a single call. 10 predefined workflows: compliance_full_audit (6 steps: KYC+sanctions+AI_gov+privacy+ESRS+CSRD), deal_due_diligence (7 steps: deep_dive+registry+court+patents+KYC+financials+M&A), market_entry_brief (6 steps: country_study+regulations+procurement+tax+AGOA+market_brief), competitor_intelligence_pack (5 steps: deep_dive+intel+patents+earnings+pitch_deck), esg_360 (5 steps: ESG_audit+carbon+CSRD+ESRS+supplier_esg), ip_freedom_to_operate (4 steps: patent_search+async_deep+IP_audit+competitive), climate_property_assessment (3 steps: climate_risk+real_estate+geo), pharma_target_screen (4 steps: trials+adverse_events+patents+meta_analysis), sanctions_360 (5 steps: KYC+Russian_sec+registry+crypto_wallet+court_filings), talent_market_brief (4 steps: salary+trends+adjacent_roles+skills_taxonomy). Returns steps_executed, consolidated P0/P1/P2 signals, overall_status, estimated_cost_usd, and raw outputs per step. Cache: 1h LRU per (workflow, target). Budget: 60s global timeout → partial if exceeded. Use when an agent needs a composite liverable without orchestrating tools manually.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "params": { "additionalProperties": true, "description": "Optional overrides passed to sub-tools. Keys depend on workflow (e.g., country, sector, role, drug, technology, wallet_address, acquirer).", "type": "object" }, "skip_failed_steps": { "description": "Default true: continue on step failure. Set false to abort on first error.", "type": "boolean" }, "target": { "description": "The entity to analyze. A company name for most workflows; location for climate_property_assessment; role+country for talent_market_brief." }, "workflow": { "description": "Named workflow to execute. Each workflow chains 3-7 tools sequentially.", "enum": [ "compliance_full_audit", "deal_due_diligence", "market_entry_brief", "competitor_intelligence_pack", "esg_360", "ip_freedom_to_operate", "climate_property_assessment", "pharma_target_screen", "sanctions_360", "talent_market_brief" ], "type": "string" } }, "required": [ "workflow", "target" ], "type": "object" }, "name": "workflow_orchestrator", "outputSchema": { "properties": { "consolidated_signals": { "items": { "properties": { "message": { "type": "string" }, "severity": { "enum": [ "P0", "P1", "P2" ], "type": "string" }, "source_tool": { "type": "string" } }, "required": [ "source_tool", "severity", "message" ], "type": "object" }, "type": "array" }, "estimated_cost_usd": { "type": "number" }, "outputs": { "type": "object" }, "overall_status": { "enum": [ "complete", "partial", "failed" ], "type": "string" }, "steps_executed": { "items": { "properties": { "args_used": {}, "completed_at": { "type": "string" }, "duration_ms": { "type": "number" }, "error": { "type": "string" }, "started_at": { "type": "string" }, "status": { "enum": [ "success", "failed", "skipped" ], "type": "string" }, "step_n": { "type": "number" }, "summary": { "type": "string" }, "tool_called": { "type": "string" } }, "required": [ "step_n", "tool_called", "args_used", "started_at", "completed_at", "duration_ms", "status" ], "type": "object" }, "type": "array" }, "summary": { "type": "string" }, "target": {}, "total_duration_ms": { "type": "number" }, "workflow": { "type": "string" } }, "required": [ "workflow", "target", "steps_executed", "summary", "consolidated_signals", "overall_status", "total_duration_ms", "estimated_cost_usd", "outputs" ], "type": "object" } }, { "description": "Optimiseur du BFR — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Reference case: Agicap — BFR optimisation · DSO 52→38j · Cash libéré +€2.8M · 3 quick wins immédiats. Inputs are validated server-side — send the documented case fields.", "inputSchema": { "additionalProperties": true, "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "challenges": { "items": { "maxLength": 300, "minLength": 5, "type": "string" }, "maxItems": 5, "minItems": 1, "type": "array" }, "company": { "properties": { "name": { "maxLength": 200, "minLength": 2, "type": "string" }, "sector": { "maxLength": 100, "minLength": 2, "type": "string" }, "stage": { "enum": [ "pre-seed", "seed", "serie-a", "serie-b", "serie-c", "serie-d-plus", "profitable-sme", "enterprise" ], "type": "string" } }, "required": [ "name", "sector", "stage" ], "type": "object" }, "financials": { "properties": { "accountsPayableEur": { "minimum": 0, "type": "number" }, "accountsReceivableEur": { "minimum": 0, "type": "number" }, "annualRevenueEur": { "minimum": 0, "type": "number" }, "currentCashEur": { "minimum": 0, "type": "number" }, "currentDioDays": { "minimum": 0, "type": "number" }, "currentDpoDays": { "minimum": 0, "type": "number" }, "currentDsoDays": { "minimum": 0, "type": "number" }, "inventoryEur": { "minimum": 0, "type": "number" } }, "required": [ "annualRevenueEur", "currentCashEur", "accountsReceivableEur", "accountsPayableEur", "currentDsoDays", "currentDpoDays" ], "type": "object" }, "industry": { "maxLength": 100, "minLength": 2, "type": "string" }, "topCustomers": { "items": { "maxLength": 100, "type": "string" }, "maxItems": 5, "minItems": 0, "type": "array" }, "topSuppliers": { "items": { "maxLength": 100, "type": "string" }, "maxItems": 5, "minItems": 0, "type": "array" } }, "required": [ "company", "financials", "challenges" ], "type": "object" }, "name": "working_capital", "outputSchema": null }, { "description": "As a CFO, assess how ESG factors (Environmental, Social, Governance) influence working capital efficiency using IMF SDR and BIS data. Inputs include company sector, geographic exposure, and ESG risk scores. Outputs provide a quantitative impact rating on working capital metrics like days sales outstanding (DSO) and inventory turnover, alongside IMF SDR-aligned liquidity risk indicators.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "currency": { "description": "Reporting currency (ISO 4217 code, e.g., 'USD', 'EUR')", "type": "string" }, "esgRiskScore": { "description": "Aggregate ESG risk score (0-100)", "type": "number" }, "region": { "description": "Primary geographic exposure (e.g., 'EU', 'APAC')", "type": "string" }, "sector": { "description": "Industry sector (e.g., 'manufacturing', 'energy')", "type": "string" }, "workingCapitalRatio": { "description": "Current working capital ratio (current assets / current liabilities)", "type": "number" } }, "required": [ "sector", "region", "esgRiskScore" ], "type": "object" }, "name": "working_capital_esg_impact_rater", "outputSchema": { "properties": { "esgFactorBreakdown": { "properties": { "environmental": { "type": "number" }, "governance": { "type": "number" }, "social": { "type": "number" } }, "type": "object" }, "impactRating": { "description": "ESG impact on working capital efficiency (-100 to +100)", "type": "number" }, "liquidityRiskIndicator": { "description": "IMF SDR-aligned liquidity risk score (0-1)", "type": "number" }, "sources": { "items": { "properties": { "data_type": { "type": "string" }, "fetched_at": { "format": "date-time", "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" }, "workingCapitalAdjustment": { "description": "Projected adjustment to working capital ratio (%)", "type": "number" } }, "required": [ "status" ], "type": "object" } }, { "description": "For CFOs managing multinational working capital, this tool analyzes real-time ECB and FRED foreign exchange rates to recommend optimal hedging strategies. Input base currency, target currencies, and working capital amounts to receive forward contract suggestions, natural hedge opportunities, and cost-benefit analysis of various hedging instruments (forwards, options, swaps). Outputs include hedge ratios, estimated cost savings, and risk reduction metrics.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "baseCurrency": { "description": "ISO 4217 code of the company's functional currency (e.g., 'USD', 'EUR')", "type": "string" }, "riskAppetite": { "default": "balanced", "description": "Company's risk tolerance for currency fluctuations", "enum": [ "conservative", "balanced", "aggressive" ], "type": "string" }, "targetCurrencies": { "description": "ISO 4217 codes of currencies to hedge against (e.g., ['EUR', 'GBP', 'JPY'])", "items": { "type": "string" }, "type": "array" }, "timeHorizonDays": { "default": 90, "description": "Planning horizon in days (default: 90)", "type": "number" }, "workingCapitalAmounts": { "additionalProperties": { "type": "number" }, "description": "Working capital amounts in each target currency (e.g., { EUR: 5000000, GBP: 3000000 })", "type": "object" } }, "required": [ "baseCurrency", "targetCurrencies", "workingCapitalAmounts" ], "type": "object" }, "name": "working_capital_fx_hedge_optimizer", "outputSchema": { "properties": { "recommendations": { "properties": { "costSavings": { "properties": { "byCurrency": { "additionalProperties": { "type": "number" }, "type": "object" }, "total": { "type": "number" } }, "type": "object" }, "hedgeRatios": { "additionalProperties": { "type": "number" }, "description": "Recommended hedge ratios (0-1) for each currency pair", "type": "object" }, "instruments": { "items": { "properties": { "currencyPair": { "type": "string" }, "maturityDays": { "type": "number" }, "notional": { "type": "number" }, "premium": { "type": "number" }, "strikeRate": { "type": "number" }, "type": { "enum": [ "forward", "option", "swap" ], "type": "string" } }, "required": [ "type", "currencyPair", "notional", "maturityDays" ], "type": "object" }, "type": "array" }, "naturalHedges": { "items": { "properties": { "amount": { "type": "number" }, "currencyPair": { "type": "string" }, "description": { "type": "string" }, "savings": { "type": "number" } }, "type": "object" }, "type": "array" }, "riskMetrics": { "properties": { "expectedShortfall": { "type": "number" }, "valueAtRisk": { "type": "number" }, "volatilityReduction": { "type": "number" } }, "type": "object" } }, "type": "object" }, "sources": { "items": { "properties": { "data_type": { "type": "string" }, "fetched_at": { "type": "string" }, "url": { "type": "string" } }, "required": [ "url", "fetched_at" ], "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Monitors real-time x402-USDC liquidity depth across 12 decentralized and centralized exchanges, providing slippage alerts and depth analysis for CFO liquidity risk assessment. Inputs include slippage thresholds and exchange selection; outputs liquidity depth, price impact estimates, and warning flags. Essential for optimizing trade execution and managing liquidity exposure. Keywords: liquidity monitoring, slippage analysis, DEX/CEX depth, x402-USDC pair, CFO financial tooling.", "inputSchema": { "properties": { "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "depthLevels": { "description": "Liquidity depth levels to analyze (percentage from mid-price)", "items": { "enum": [ 0.1, 0.5, 1, 2, 5, 10 ], "type": "number" }, "type": "array" }, "exchanges": { "description": "List of exchanges to monitor (defaults to all 12 if empty)", "items": { "enum": [ "uniswap", "1inch", "coinbase", "binance", "kraken", "okx", "huobi", "bybit", "gateio", "mexc", "bitfinex", "bitstamp" ], "type": "string" }, "type": "array" }, "slippageThreshold": { "description": "Maximum acceptable slippage percentage (0-100)", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "slippageThreshold" ], "type": "object" }, "name": "x402_liquidity_monitor", "outputSchema": { "properties": { "liquidityDepth": { "properties": { "combined": { "description": "Aggregated liquidity across all exchanges", "type": "number" }, "oneinch": { "description": "Total x402-USDC liquidity in USD on 1inch", "type": "number" }, "uniswap": { "description": "Total x402-USDC liquidity in USD on Uniswap", "type": "number" } }, "type": "object" }, "midPrice": { "description": "Current x402-USDC mid-price", "type": "number" }, "priceImpact": { "properties": { "current": { "type": "number" }, "worstCase": { "type": "number" } }, "type": "object" }, "slippageAlerts": { "items": { "properties": { "depthLevel": { "type": "number" }, "exceedsThreshold": { "type": "boolean" }, "exchange": { "type": "string" }, "slippage": { "type": "number" } }, "type": "object" }, "type": "array" }, "sources": { "items": { "properties": { "exchange": { "type": "string" }, "fetched_at": { "format": "date-time", "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status", "liquidityDepth", "slippageAlerts", "warnings", "sources" ], "type": "object" } }, { "description": "As a CTO, analyze USDC payment flows involving x402 addresses to assess counterparty risk, trace transaction paths, and evaluate regulatory exposure. Input a wallet address or transaction hash to receive risk scores, flow diagrams, and compliance flags from Chainalysis and TRM Labs public APIs. Ideal for due diligence, fraud detection, and compliance reporting. Pass async:true to avoid timeout.", "inputSchema": { "properties": { "address": { "description": "Ethereum wallet address to analyze", "pattern": "^0x[a-fA-F0-9]{40}$", "type": "string" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "depth": { "default": 3, "description": "Hops to trace in payment flow", "maximum": 5, "minimum": 1, "type": "number" }, "includeRiskScore": { "default": true, "description": "Include counterparty risk scoring", "type": "boolean" }, "txHash": { "description": "USDC transaction hash to trace", "pattern": "^0x[a-fA-F0-9]{64}$", "type": "string" } }, "required": [ "address" ], "type": "object" }, "name": "x402_payment_flow_analyzer", "outputSchema": { "properties": { "complianceFlags": { "items": { "properties": { "description": { "type": "string" }, "flag": { "type": "string" }, "severity": { "enum": [ "info", "warning", "critical" ], "type": "string" } }, "type": "object" }, "type": "array" }, "exposureSummary": { "properties": { "counterparties": { "type": "number" }, "highRiskExposure": { "type": "string" }, "totalExposure": { "type": "string" } }, "type": "object" }, "flowId": { "description": "Unique identifier for this payment flow analysis", "type": "string" }, "riskScore": { "description": "Counterparty risk score (0-100)", "maximum": 100, "minimum": 0, "type": "number" }, "sources": { "items": { "properties": { "fetched_at": { "format": "date-time", "type": "string" }, "source": { "enum": [ "chainalysis", "trm_labs", "etherscan" ], "type": "string" }, "url": { "type": "string" } }, "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "transactionPath": { "items": { "properties": { "amount": { "type": "string" }, "from": { "type": "string" }, "riskLevel": { "enum": [ "low", "medium", "high", "critical" ], "type": "string" }, "timestamp": { "format": "date-time", "type": "string" }, "to": { "type": "string" } }, "required": [ "from", "to", "amount", "timestamp" ], "type": "object" }, "type": "array" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status" ], "type": "object" } }, { "description": "Risk-focused tool that analyzes x402-USDC payment transactions for fraud patterns using on-chain forensics. Takes a transaction hash or wallet address as input and returns risk scores, suspicious indicators, and historical patterns. Designed for risk management teams to quickly assess payment legitimacy. Includes keywords: fraud detection, USDC risk, blockchain forensics, transaction monitoring. pass async:true to avoid timeout.", "inputSchema": { "properties": { "amountThreshold": { "minimum": 0, "type": "number" }, "async": { "description": "If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.", "type": "boolean" }, "includeHistory": { "default": false, "type": "boolean" }, "transactionHash": { "pattern": "^0x[a-fA-F0-9]{64}$", "type": "string" }, "walletAddress": { "pattern": "^0x[a-fA-F0-9]{40}$", "type": "string" } }, "required": [ "transactionHash" ], "type": "object" }, "name": "x402_payment_fraud_detector", "outputSchema": { "properties": { "fraudIndicators": { "items": { "properties": { "confidence": { "maximum": 1, "minimum": 0, "type": "number" }, "description": { "type": "string" }, "type": { "type": "string" } }, "required": [ "type", "confidence", "description" ], "type": "object" }, "type": "array" }, "isSuspicious": { "type": "boolean" }, "riskScore": { "maximum": 100, "minimum": 0, "type": "number" }, "sanctionsMatch": { "type": "boolean" }, "sources": { "items": { "type": "object" }, "type": "array" }, "status": { "enum": [ "final", "partial", "failed" ], "type": "string" }, "transactionHistory": { "items": { "properties": { "amount": { "type": "number" }, "counterparty": { "pattern": "^0x[a-fA-F0-9]{40}$", "type": "string" }, "riskScore": { "maximum": 100, "minimum": 0, "type": "number" }, "timestamp": { "format": "date-time", "type": "string" } }, "required": [ "timestamp", "amount", "counterparty" ], "type": "object" }, "type": "array" }, "warnings": { "items": { "type": "string" }, "type": "array" } }, "required": [ "status", "riskScore", "isSuspicious", "warnings", "sources" ], "type": "object" } } ] }
Verify it yourselfcurl -s https://api.teppi.xyz/v1/evidence/sha256:cc0b73694c30b7a97a6dc327cd45b6df3c7cd0caba044f33c3c01436c1697a8c | sha256sum