Server definition

Hash: sha256:25ec3f0b566b3692af377944787921f25a3815b110d25bbbeb53bee352d1a64a
What it is: What a remote MCP server returned when asked what it offers: 27 tools

The blob, as servednamed by its sha256

{
  "instructions": "LocalIntel gives you market intelligence for any Florida ZIP. All tools below are FREE — no key required. Start with local_intel_ask for any plain-English question (e.g. \"What food gaps exist in Tampa?\", \"Is 33602 oversaturated with gyms?\"). Use local_intel_sector_gap to find structural business whitespace — NAICS sectors present at county but absent at ZIP, with ranked demand estimates. Use local_intel_signal for a 0-100 investment score. Use local_intel_zone for demographics (population, HHI, income tier, ownership rate). Use local_intel_tide for momentum scoring. Vertical agents answer domain questions: local_intel_restaurant, local_intel_healthcare, local_intel_retail, local_intel_construction, local_intel_realtor. All tools are read-only. Data: 1,473 FL ZIPs, 240k+ businesses, ACS + IRS + CBP + OSM + BLS + FDOT.",
  "tools": [
    {
      "description": "Composite NL query layer. Ask any plain-English question about a ZIP — demographics, market opportunity, restaurant gaps, retail saturation, construction activity, investment signals, healthcare, corridor analysis, recent changes, nearby businesses. Routes internally to the right tools and returns a synthesized, sourced answer with confidence score. Best single entry point for humans and LLMs.",
      "inputSchema": {
        "properties": {
          "question": {
            "description": "Plain English question, e.g. \"What restaurant categories are missing in 32082?\"",
            "type": "string"
          },
          "zip": {
            "description": "ZIP code (optional — will be extracted from question if present, defaults to 32082)",
            "type": "string"
          }
        },
        "required": [
          "question"
        ],
        "type": "object"
      },
      "name": "local_intel_ask",
      "outputSchema": null
    },
    {
      "description": "Infrastructure momentum score and active leading indicators for a ZIP from Layer 0. Permits, road projects, flood zones, utility extensions. Predicts conditions 12-36 months ahead. 'Let Google pay for the satellites — we sell the weather forecast.'",
      "inputSchema": {
        "properties": {
          "query_context": {
            "description": "Optional: { agent_type, agent_id }",
            "type": "object"
          },
          "zip": {
            "description": "ZIP code",
            "type": "string"
          }
        },
        "required": [
          "zip"
        ],
        "type": "object"
      },
      "name": "local_intel_bedrock",
      "outputSchema": null
    },
    {
      "description": "Book a specific response to an RFQ — confirms the job with that business. Use after reviewing local_intel_rfq_status responses.",
      "inputSchema": {
        "properties": {
          "note": {
            "description": "Optional note to the business",
            "type": "string"
          },
          "response_id": {
            "description": "UUID of the response to accept",
            "type": "string"
          },
          "rfq_id": {
            "description": "UUID of the RFQ",
            "type": "string"
          }
        },
        "required": [
          "rfq_id",
          "response_id"
        ],
        "type": "object"
      },
      "name": "local_intel_book",
      "outputSchema": null
    },
    {
      "description": "Recently added or owner-verified business listings. Use to detect new openings or data updates.",
      "inputSchema": {
        "properties": {
          "limit": {
            "description": "Max results (default 20)",
            "type": "integer"
          },
          "zip": {
            "description": "Optional ZIP filter",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "local_intel_changes",
      "outputSchema": null
    },
    {
      "description": "Compare up to 10 ZIP codes side-by-side and get a ranked opportunity table. Returns per-ZIP signals (HHI, capture rate, infra momentum, consumer profile, top gap) plus a top_pick recommendation with reasoning. Best tool for site selection, franchise expansion, investment screening, and market prioritization.",
      "inputSchema": {
        "properties": {
          "focus": {
            "description": "Ranking focus: \"opportunity\" (default), \"hhi\", \"saturation\", \"growth\", or \"population\".",
            "type": "string"
          },
          "limit": {
            "description": "Max rows to return (default 10).",
            "type": "number"
          },
          "zips": {
            "description": "Array of ZIP codes to compare, e.g. [\"32082\",\"32081\",\"32084\"]. Max 10.",
            "items": {
              "type": "string"
            },
            "type": "array"
          }
        },
        "required": [
          "zips"
        ],
        "type": "object"
      },
      "name": "local_intel_compare",
      "outputSchema": null
    },
    {
      "description": "Mark a booked job as complete and settle payment to the local merchant wallet (Tempo pathUSD when SETTLEMENT_ENABLED=true; otherwise records settled_intent and feeds the forecast loop).",
      "inputSchema": {
        "properties": {
          "booking_id": {
            "description": "UUID returned by local_intel_book",
            "type": "string"
          },
          "note": {
            "description": "Completion note or rating",
            "type": "string"
          }
        },
        "required": [
          "booking_id"
        ],
        "type": "object"
      },
      "name": "local_intel_complete",
      "outputSchema": null
    },
    {
      "description": "Construction and home services market intelligence for a ZIP. Ask about contractor density, active permits, housing starts, population growth driving demand. Returns structured data with confidence score. Trained on 100 construction business prompts.",
      "inputSchema": {
        "properties": {
          "lat": {
            "description": "Latitude (WGS84) — resolves to nearest FL ZIP",
            "type": "number"
          },
          "lon": {
            "description": "Longitude (WGS84)",
            "type": "number"
          },
          "query": {
            "description": "Natural language question about construction market",
            "type": "string"
          },
          "zip": {
            "description": "ZIP code to analyze",
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "local_intel_construction",
      "outputSchema": null
    },
    {
      "description": "Full spatial context block for any FL zip or lat/lon. Returns anchor business, nearby businesses in distance rings, zone intelligence, and category breakdown. Best first call for any location query. Covers all 1,473 FL ZIPs via fl_zip_geo.",
      "inputSchema": {
        "properties": {
          "lat": {
            "description": "Latitude (WGS84) — resolves to nearest FL ZIP",
            "type": "number"
          },
          "lon": {
            "description": "Longitude (WGS84) — required if lat is provided",
            "type": "number"
          },
          "radius_miles": {
            "description": "Search radius in miles (default 1.0)",
            "type": "number"
          },
          "zip": {
            "description": "Any FL ZIP code",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "local_intel_context",
      "outputSchema": null
    },
    {
      "description": "Businesses along a named street corridor. Use for queries like \"what is on A1A\" or \"businesses on Palm Valley Road\".",
      "inputSchema": {
        "properties": {
          "limit": {
            "description": "Max results (default 20)",
            "type": "integer"
          },
          "street": {
            "description": "Street name (e.g. \"A1A\", \"Palm Valley\", \"Crosswater\")",
            "type": "string"
          },
          "zip": {
            "description": "Optional ZIP filter",
            "type": "string"
          }
        },
        "required": [
          "street"
        ],
        "type": "object"
      },
      "name": "local_intel_corridor",
      "outputSchema": null
    },
    {
      "description": "Decline a specific response to an RFQ and get the next in queue. Use when a client rejects the first responder — returns the next pending response automatically. First come first served queue.",
      "inputSchema": {
        "properties": {
          "reason": {
            "description": "Optional reason for declining (e.g. price too high, too far)",
            "type": "string"
          },
          "response_id": {
            "description": "UUID of the response to decline",
            "type": "string"
          },
          "rfq_id": {
            "description": "UUID of the RFQ",
            "type": "string"
          }
        },
        "required": [
          "rfq_id",
          "response_id"
        ],
        "type": "object"
      },
      "name": "local_intel_decline_response",
      "outputSchema": null
    },
    {
      "description": "PREMIUM composite entry point ($0.05). Declare your agent_type and intent, receive pre-ranked top-10 signals assembled from all 4 data layers, personalized for your use case. Includes delta since your last query if agent_id provided. Best first call for any new agent.",
      "inputSchema": {
        "properties": {
          "agent_id": {
            "description": "Your agent UUID for memory + delta computation",
            "type": "string"
          },
          "agent_type": {
            "description": "real_estate | financial | ad_placement | logistics | business_owner | civic",
            "type": "string"
          },
          "budget": {
            "description": "Agent budget in pathUSD (optional, for signal prioritization)",
            "type": "number"
          },
          "depth": {
            "description": "quick (top 5 signals) | full (top 10 + context blocks)",
            "type": "string"
          },
          "intent": {
            "description": "Plain-language description of what you are trying to decide or do",
            "type": "string"
          },
          "lat": {
            "description": "Latitude (if no ZIP)",
            "type": "number"
          },
          "lon": {
            "description": "Longitude (if no ZIP)",
            "type": "number"
          },
          "zip": {
            "description": "Target ZIP code",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "local_intel_for_agent",
      "outputSchema": null
    },
    {
      "description": "Healthcare market intelligence for a ZIP. Ask about provider density, patient demographics, demand gaps, senior population. Returns structured data with confidence score. Trained on 100 healthcare business prompts.",
      "inputSchema": {
        "properties": {
          "lat": {
            "description": "Latitude (WGS84) — resolves to nearest FL ZIP",
            "type": "number"
          },
          "lon": {
            "description": "Longitude (WGS84)",
            "type": "number"
          },
          "query": {
            "description": "Natural language question about healthcare market",
            "type": "string"
          },
          "zip": {
            "description": "ZIP code to analyze",
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "local_intel_healthcare",
      "outputSchema": null
    },
    {
      "description": "Find businesses within a radius of any lat/lon point, sorted by distance with compass bearing.",
      "inputSchema": {
        "properties": {
          "category": {
            "description": "Filter by OSM category",
            "type": "string"
          },
          "group": {
            "description": "Filter by semantic group",
            "type": "string"
          },
          "lat": {
            "description": "Latitude of center point",
            "type": "number"
          },
          "limit": {
            "description": "Max results (default 15)",
            "type": "integer"
          },
          "lon": {
            "description": "Longitude of center point",
            "type": "number"
          },
          "radius_miles": {
            "description": "Search radius in miles (default 0.5)",
            "type": "number"
          }
        },
        "required": [
          "lat",
          "lon"
        ],
        "type": "object"
      },
      "name": "local_intel_nearby",
      "outputSchema": null
    },
    {
      "description": "Pre-baked economic oracle for a ZIP. Returns: restaurant saturation (is there room for another?), price-tier gap analysis (what menu price is missing?), growth trajectory (growing/empty-nest/stable), and 3 pre-formed questions with answers baked in. No LLM needed — answers derived from population, income, business density, school count, and infrastructure signals.",
      "inputSchema": {
        "properties": {
          "zip": {
            "description": "ZIP code to analyze (e.g. 32081)",
            "type": "string"
          }
        },
        "required": [
          "zip"
        ],
        "type": "object"
      },
      "name": "local_intel_oracle",
      "outputSchema": null
    },
    {
      "description": "Project-type intelligence: pass a project_type (restaurant, clinic, banking, construction, real_estate, residential_development, fitness, legal, retail, auto, etc.) and get L1 ZIPs ranked by market or residential opportunity score plus L2 matching verified businesses already operating in that sector. Returns sector gap counts, HHI, population, growth state, and new-build %. Best tool for site selection and franchise expansion when you know the business type but not the ZIP.",
      "inputSchema": {
        "properties": {
          "limit": {
            "description": "Number of L1 ZIPs to return (default 5, max 10).",
            "type": "number"
          },
          "project_type": {
            "description": "Business type or project category. Examples: restaurant, clinic, banking, construction, real_estate, residential_development, fitness, legal, retail, grocery, auto, beauty, pets.",
            "type": "string"
          },
          "zip": {
            "description": "Optional. Filter L2 businesses to a specific ZIP. If omitted, returns top ZIPs ranked by score.",
            "type": "string"
          }
        },
        "required": [
          "project_type"
        ],
        "type": "object"
      },
      "name": "local_intel_project",
      "outputSchema": null
    },
    {
      "description": "START HERE. Natural language entry point for both market intelligence AND business routing. Ask about a market, find a business, or route a customer request. Auto-detects ZIP, industry vertical, and intent. For customer agents: \"Find a restaurant in 32082 that serves lunch\" or \"Who can do landscaping in Ponte Vedra?\" — returns the matching business so your agent can route the order to them. For market intel: \"Is 32082 oversaturated with dentists?\" ZIP is always required for routing — pass it explicitly or include it in the query.",
      "inputSchema": {
        "properties": {
          "lat": {
            "description": "Optional latitude (WGS84). Resolves to nearest FL ZIP. Use instead of zip for coordinate-based queries.",
            "type": "number"
          },
          "lon": {
            "description": "Optional longitude (WGS84). Required if lat is provided.",
            "type": "number"
          },
          "query": {
            "description": "Any plain-English market question. ZIP can be in the query or passed separately.",
            "type": "string"
          },
          "zip": {
            "description": "Optional ZIP override. If omitted, ZIP is detected from the query or resolved from lat/lon.",
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "local_intel_query",
      "outputSchema": null
    },
    {
      "description": "Real estate intelligence for a ZIP. Ask natural-language questions: demographics, commercial gaps, flood risk, school proximity, infrastructure signals, market saturation. Returns structured data with confidence score. Trained on 100 realtor use-case prompts.",
      "inputSchema": {
        "properties": {
          "lat": {
            "description": "Latitude (WGS84) — resolves to nearest FL ZIP",
            "type": "number"
          },
          "lon": {
            "description": "Longitude (WGS84)",
            "type": "number"
          },
          "query": {
            "description": "Natural language question (e.g. \"What is the flood risk for this ZIP?\", \"What commercial gaps exist?\")",
            "type": "string"
          },
          "zip": {
            "description": "ZIP code to analyze",
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "local_intel_realtor",
      "outputSchema": null
    },
    {
      "description": "Restaurant and food service market intelligence for a ZIP. Ask about saturation scores, price-tier gaps, capture rates, corridor analysis, tidal momentum. Returns structured data with confidence score. Trained on 100 restaurant business prompts.",
      "inputSchema": {
        "properties": {
          "lat": {
            "description": "Latitude (WGS84) — resolves to nearest FL ZIP",
            "type": "number"
          },
          "lon": {
            "description": "Longitude (WGS84)",
            "type": "number"
          },
          "query": {
            "description": "Natural language question about restaurant market",
            "type": "string"
          },
          "zip": {
            "description": "ZIP code to analyze",
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "local_intel_restaurant",
      "outputSchema": null
    },
    {
      "description": "Retail market intelligence for a ZIP. Ask about store categories, spending capture rates, consumer profile, undersupplied niches. Returns structured data with confidence score. Trained on 100 retail business prompts.",
      "inputSchema": {
        "properties": {
          "lat": {
            "description": "Latitude (WGS84) — resolves to nearest FL ZIP",
            "type": "number"
          },
          "lon": {
            "description": "Longitude (WGS84)",
            "type": "number"
          },
          "query": {
            "description": "Natural language question about retail market",
            "type": "string"
          },
          "zip": {
            "description": "ZIP code to analyze",
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "local_intel_retail",
      "outputSchema": null
    },
    {
      "description": "Route a customer request to local businesses — food orders, delivery, services, or any job. ALWAYS include the full order or ask in description (or items[]), plus business_id/business_name when ordering from a specific place. Never send a vague description like \"Buy me.\" Use this when KDS/POS is off or for quote collection. Supports delivery (first-to-accept) and proposal (collect quotes) modes.",
      "inputSchema": {
        "properties": {
          "autonomy": {
            "description": "full=agent books automatically; approve=agent picks best, human confirms; human=human picks from list",
            "enum": [
              "full",
              "approve",
              "human"
            ],
            "type": "string"
          },
          "budget_usd": {
            "description": "Max budget in USD (optional)",
            "type": "number"
          },
          "business_id": {
            "description": "Target a specific LocalIntel business (required when ordering from a named restaurant)",
            "type": "string"
          },
          "business_name": {
            "description": "Human business name, e.g. McFlamingo — shown on the Jobs card",
            "type": "string"
          },
          "category": {
            "description": "Business category to match, e.g. \"restaurant\", \"food\", \"delivery\", \"landscaping\", \"florist\", \"handyman\", \"plumber\"",
            "type": "string"
          },
          "customer_note": {
            "description": "Extra note for the business (allergies, ETA, pickup vs delivery)",
            "type": "string"
          },
          "deadline_minutes": {
            "description": "Minutes until deadline (for urgent delivery jobs)",
            "type": "number"
          },
          "description": {
            "description": "Full human-readable request. For food: include items, e.g. \"Order for McFlamingo: chicken and broccoli\". Do NOT use vague text like \"Buy me.\"",
            "type": "string"
          },
          "dropoff_address": {
            "description": "Drop-off address (delivery jobs)",
            "type": "string"
          },
          "dry_run": {
            "description": "If true, match businesses but do NOT send email/SMS/push/rail notifications. Also auto-enabled for x-agent-id values starting with cursor-test-, test-, agent-test-, or dry-run.",
            "type": "boolean"
          },
          "items": {
            "description": "Structured line items, e.g. [{ \"name\": \"chicken and broccoli\", \"qty\": 1 }]",
            "items": {
              "type": "object"
            },
            "type": "array"
          },
          "job_type": {
            "description": "delivery = first-to-accept wins (food orders, pickups); proposal = collect quotes, pick best (services, construction)",
            "enum": [
              "delivery",
              "proposal"
            ],
            "type": "string"
          },
          "notify_email": {
            "description": "Email to notify for approve/human autonomy levels",
            "type": "string"
          },
          "pickup_address": {
            "description": "Pickup address (delivery jobs)",
            "type": "string"
          },
          "task": {
            "description": "Alias for description (same meaning)",
            "type": "string"
          },
          "zip": {
            "description": "ZIP code to search businesses in",
            "type": "string"
          }
        },
        "required": [
          "description"
        ],
        "type": "object"
      },
      "name": "local_intel_rfq",
      "outputSchema": null
    },
    {
      "description": "Poll the status of an RFQ. Returns the original request, all responses received so far, and booking details if booked.",
      "inputSchema": {
        "properties": {
          "rfq_id": {
            "description": "UUID returned by local_intel_rfq",
            "type": "string"
          }
        },
        "required": [
          "rfq_id"
        ],
        "type": "object"
      },
      "name": "local_intel_rfq_status",
      "outputSchema": null
    },
    {
      "description": "Search businesses by name, category, or semantic group (food, retail, health, finance, civic, services).",
      "inputSchema": {
        "properties": {
          "category": {
            "description": "Exact OSM category (restaurant, bank, dentist...)",
            "type": "string"
          },
          "group": {
            "description": "Semantic group: food | retail | health | finance | civic | services",
            "type": "string"
          },
          "limit": {
            "description": "Max results (default 20, max 50)",
            "type": "integer"
          },
          "query": {
            "description": "Text search on name/category/address",
            "type": "string"
          },
          "zip": {
            "description": "Filter by ZIP code",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "local_intel_search",
      "outputSchema": null
    },
    {
      "description": "Ranked sector gap analysis for a ZIP. Identifies NAICS sectors present at county level (CBP/CES employment) but underrepresented at ZIP (OSM business counts) — the structural whitespace in a local economy. Returns ranked opportunities with: NAICS code, sector label, county employment share, demand estimate, confidence tier, and LLM-ready signal narrative. Reads live from Postgres zip_signals — always current. Example: \"NAICS 62 Health Care: Jacksonville MSA 136k healthcare employees, ZIP 32082 has no OSM healthcare listings. 28,697 residents, $121k median HHI, retiree index 1.5x. Demand: 7–10 providers.\" Chain into vertical agents via oracle_vertical. Cost: $0.03 pathUSD.",
      "inputSchema": {
        "properties": {
          "zip": {
            "description": "ZIP code to analyze (e.g. 32081, 32082, 32259)",
            "type": "string"
          }
        },
        "required": [
          "zip"
        ],
        "type": "object"
      },
      "name": "local_intel_sector_gap",
      "outputSchema": null
    },
    {
      "description": "Investment and activity signal for a ZIP. Composite score 0-100 with band (strong_buy/accumulate/hold/reduce/avoid), top reasons, and avoid flags. Best for real estate and financial agents.",
      "inputSchema": {
        "properties": {
          "agent_type": {
            "description": "real_estate | financial | ad_placement | logistics | business_owner | civic",
            "type": "string"
          },
          "query_context": {
            "description": "Optional: { agent_id, purpose }",
            "type": "object"
          },
          "zip": {
            "description": "ZIP code",
            "type": "string"
          }
        },
        "required": [
          "zip"
        ],
        "type": "object"
      },
      "name": "local_intel_signal",
      "outputSchema": null
    },
    {
      "description": "Dataset coverage stats: total businesses, confidence scores, query volume, revenue earned.",
      "inputSchema": {
        "properties": {},
        "type": "object"
      },
      "name": "local_intel_stats",
      "outputSchema": null
    },
    {
      "description": "Tidal reading for a ZIP — temperature (0-100), direction (surging/heating/stable/cooling/receding), seasonal context. Synthesizes all 4 data layers. Best for agents deciding WHERE to act next.",
      "inputSchema": {
        "properties": {
          "include_layers": {
            "description": "Layers to include: bedrock, ocean_floor, surface_current, wave_surface (default: all)",
            "type": "array"
          },
          "query_context": {
            "description": "Optional: { agent_type, agent_id, purpose }",
            "type": "object"
          },
          "zip": {
            "description": "ZIP code to read tidal state for",
            "type": "string"
          }
        },
        "required": [
          "zip"
        ],
        "type": "object"
      },
      "name": "local_intel_tide",
      "outputSchema": null
    },
    {
      "description": "Spending zone and demographic data for a ZIP code: population, income, home value, rent, ownership rate, zone score. Pass zip or lat/lon.",
      "inputSchema": {
        "properties": {
          "lat": {
            "description": "Latitude (WGS84) — resolves to nearest FL ZIP",
            "type": "number"
          },
          "lon": {
            "description": "Longitude (WGS84) — required if lat is provided",
            "type": "number"
          },
          "zip": {
            "description": "FL ZIP code",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "local_intel_zone",
      "outputSchema": null
    }
  ]
}

Verify it yourself

curl -s https://api.teppi.xyz/v1/evidence/sha256:25ec3f0b566b3692af377944787921f25a3815b110d25bbbeb53bee352d1a64a | sha256sum