Server definition

Hash: sha256:d34f9e2abe03634ee56448e9f070c73f4c3ef8f98cf23f19def9964ae30ce225
What it is: What a remote MCP server returned when asked what it offers: 19 tools

The blob, as servednamed by its sha256

{
  "instructions": null,
  "tools": [
    {
      "description": "Score candidate actions and extract robust action zones.",
      "inputSchema": {
        "properties": {
          "action_labels": {
            "description": "Optional labels for each action variant.",
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "action_tests": {
            "description": "1+ candidate actions/scenarios to score against baseline.",
            "minItems": 1,
            "type": "array"
          },
          "encoder_type": {
            "description": "Optional encoder override. Omit to auto-detect.",
            "type": "string"
          },
          "field_level": {
            "description": "0 = real scalar field, 1 = complex field.",
            "enum": [
              0,
              1
            ],
            "type": "integer"
          },
          "sensitivity": {
            "description": "Anomaly sensitivity multiplier (default: 1.0).",
            "type": "number"
          },
          "training": {
            "description": "2+ baseline normal samples used to define the reference profile.",
            "minItems": 2,
            "type": "array"
          }
        },
        "required": [
          "training",
          "action_tests"
        ],
        "type": "object"
      },
      "name": "waveguard_action_surface",
      "outputSchema": null
    },
    {
      "description": "Estimate shock propagation and resilience from adjacency-linked entities.",
      "inputSchema": {
        "properties": {
          "adjacency_matrix": {
            "description": "N×N weighted adjacency matrix describing link strengths between entities.",
            "items": {
              "items": {
                "type": "number"
              },
              "type": "array"
            },
            "type": "array"
          },
          "encoder_type": {
            "description": "Optional encoder override. Omit to auto-detect.",
            "type": "string"
          },
          "entities": {
            "description": "2+ entities/nodes participating in the cascade graph.",
            "minItems": 2,
            "type": "array"
          },
          "field_level": {
            "default": 1,
            "description": "Field representation level. Default 1 for graph interaction dynamics.",
            "enum": [
              0,
              1
            ],
            "type": "integer"
          },
          "sensitivity": {
            "description": "Anomaly sensitivity multiplier (default: 1.0).",
            "type": "number"
          },
          "shock_indices": {
            "description": "Indices of initially shocked entities within the entities array.",
            "items": {
              "type": "integer"
            },
            "type": "array"
          },
          "shock_strength": {
            "default": 0.05,
            "description": "Initial perturbation magnitude injected at shock indices.",
            "type": "number"
          },
          "training_context": {
            "description": "2+ baseline context samples used for normalization.",
            "minItems": 2,
            "type": "array"
          }
        },
        "required": [
          "training_context",
          "entities",
          "adjacency_matrix",
          "shock_indices"
        ],
        "type": "object"
      },
      "name": "waveguard_cascade_risk",
      "outputSchema": null
    },
    {
      "description": "Compare two data items for structural similarity using physics-based fingerprints. Returns cosine similarity (0–1) and Euclidean distance. Use for duplicate detection, behavioral matching, drift analysis, or checking if two tokens/wallets/contracts are structurally similar.\n\nCosine similarity > 0.95 = very similar. < 0.80 = structurally different.",
      "inputSchema": {
        "properties": {
          "data_a": {
            "description": "First data item to compare."
          },
          "data_b": {
            "description": "Second data item to compare (same type as data_a)."
          },
          "encoder_type": {
            "description": "Data encoder. Omit to auto-detect.",
            "enum": [
              "json",
              "numeric",
              "text",
              "timeseries",
              "tabular"
            ],
            "type": "string"
          }
        },
        "required": [
          "data_a",
          "data_b"
        ],
        "type": "object"
      },
      "name": "waveguard_compare",
      "outputSchema": null
    },
    {
      "description": "Run baseline plus counterfactual variants and measure verdict/score sensitivity.",
      "inputSchema": {
        "properties": {
          "base_test": {
            "description": "Baseline candidate sample to evaluate before counterfactual perturbations."
          },
          "counterfactual_tests": {
            "description": "1+ perturbed variants of base_test for sensitivity analysis.",
            "minItems": 1,
            "type": "array"
          },
          "encoder_type": {
            "description": "Optional encoder override. Omit to auto-detect from input structure.",
            "type": "string"
          },
          "field_level": {
            "description": "0 = real scalar field (faster), 1 = complex field (richer phase dynamics).",
            "enum": [
              0,
              1
            ],
            "type": "integer"
          },
          "sensitivity": {
            "description": "Anomaly sensitivity multiplier (default: 1.0). Higher values flag more aggressively.",
            "type": "number"
          },
          "training": {
            "description": "2+ baseline normal samples used to build the reference profile.",
            "minItems": 2,
            "type": "array"
          }
        },
        "required": [
          "training",
          "base_test",
          "counterfactual_tests"
        ],
        "type": "object"
      },
      "name": "waveguard_counterfactual",
      "outputSchema": null
    },
    {
      "description": "Get a physics embedding of any data item (52-dim at Level 0, 62-dim at Level 1 with phase statistics). The fingerprint captures structural properties via wave-equation dynamics — useful for similarity search, clustering, baseline comparison, and drift detection. Works on JSON objects, token metrics, wallet activity, trading data, or any structured data.\n\nReturns a deterministic vector with labeled dimensions (chi statistics, energy distribution, gradient patterns, and phase coherence at Level 1).",
      "inputSchema": {
        "properties": {
          "data": {
            "description": "Any data item to fingerprint: JSON object, numeric array, string, or structured record."
          },
          "encoder_type": {
            "description": "Data encoder. Omit to auto-detect.",
            "enum": [
              "json",
              "numeric",
              "text",
              "timeseries",
              "tabular",
              "complex_numeric"
            ],
            "type": "string"
          },
          "field_level": {
            "description": "0 = real scalar 52-dim (default), 1 = complex field 62-dim.",
            "enum": [
              0,
              1
            ],
            "type": "integer"
          }
        },
        "required": [
          "data"
        ],
        "type": "object"
      },
      "name": "waveguard_fingerprint",
      "outputSchema": null
    },
    {
      "description": "Check WaveGuard API health, GPU availability, version, and engine status. No authentication required. Returns status, version, and GPU info.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "verbose": {
            "default": false,
            "description": "Return detailed health info including memory and uptime (default: false).",
            "type": "boolean"
          }
        },
        "type": "object"
      },
      "name": "waveguard_health",
      "outputSchema": null
    },
    {
      "description": "Estimate instability under controlled perturb-and-resolve trials.",
      "inputSchema": {
        "properties": {
          "encoder_type": {
            "description": "Optional encoder override. Omit to auto-detect.",
            "type": "string"
          },
          "field_level": {
            "description": "0 = real scalar field, 1 = complex field.",
            "enum": [
              0,
              1
            ],
            "type": "integer"
          },
          "perturbation_strength": {
            "default": 0.02,
            "description": "Relative perturbation amplitude applied during instability assay.",
            "type": "number"
          },
          "sensitivity": {
            "description": "Anomaly sensitivity multiplier (default: 1.0).",
            "type": "number"
          },
          "test": {
            "description": "1+ candidate samples to stress-test with perturbation trials.",
            "minItems": 1,
            "type": "array"
          },
          "training": {
            "description": "2+ baseline normal samples for reference dynamics.",
            "minItems": 2,
            "type": "array"
          },
          "trials": {
            "default": 12,
            "description": "Number of perturbation trials per sample.",
            "minimum": 1,
            "type": "integer"
          }
        },
        "required": [
          "training",
          "test"
        ],
        "type": "object"
      },
      "name": "waveguard_instability",
      "outputSchema": null
    },
    {
      "description": "Compute pairwise interaction matrix and cluster decomposition for entities.",
      "inputSchema": {
        "properties": {
          "encoder_type": {
            "description": "Optional encoder override. Omit to auto-detect.",
            "type": "string"
          },
          "entities": {
            "description": "2+ entities to evaluate for pairwise interaction effects.",
            "minItems": 2,
            "type": "array"
          },
          "field_level": {
            "default": 1,
            "description": "Field representation level. Default 1 for interaction/phase features.",
            "enum": [
              0,
              1
            ],
            "type": "integer"
          },
          "sensitivity": {
            "description": "Anomaly sensitivity multiplier (default: 1.0).",
            "type": "number"
          },
          "training_context": {
            "description": "2+ baseline context samples used for normalization.",
            "minItems": 2,
            "type": "array"
          }
        },
        "required": [
          "training_context",
          "entities"
        ],
        "type": "object"
      },
      "name": "waveguard_interaction_matrix",
      "outputSchema": null
    },
    {
      "description": "Fetch live crypto market data from CoinGecko and DexScreener. No external data needed — WaveGuard pulls it for you.\n\nUse 'coin_id' for CoinGecko (e.g. 'bitcoin', 'ethereum', 'solana').\nUse 'contract_address' for DexScreener (any chain).\nUse 'search' to find token IDs by name/symbol.\n\nReturns: price, volume, market cap, liquidity, price history, OHLC candles — ready to feed into waveguard_token_risk, waveguard_volume_check, or waveguard_price_manipulation.",
      "inputSchema": {
        "properties": {
          "action": {
            "description": "What data to fetch:\n- token_data: full metrics for a CoinGecko coin\n- price_history: daily prices (for price_manipulation)\n- ohlc: OHLC candles (for volume_check)\n- top_coins: top N by market cap (training baseline)\n- search: find CoinGecko coin IDs\n- dex_token: DEX data by contract address\n- dex_search: search DEX pairs",
            "enum": [
              "token_data",
              "price_history",
              "ohlc",
              "top_coins",
              "search",
              "dex_token",
              "dex_search"
            ],
            "type": "string"
          },
          "coin_id": {
            "description": "CoinGecko coin ID (e.g. 'bitcoin', 'ethereum'). Required for token_data, price_history, ohlc.",
            "type": "string"
          },
          "contract_address": {
            "description": "Token contract address (any chain). Required for dex_token.",
            "type": "string"
          },
          "count": {
            "default": 25,
            "description": "Number of results for top_coins (default: 25).",
            "type": "integer"
          },
          "days": {
            "default": 90,
            "description": "Number of days of history (default: 90 for price_history, 30 for ohlc).",
            "type": "integer"
          },
          "query": {
            "description": "Search query. Required for search, dex_search.",
            "type": "string"
          }
        },
        "required": [
          "action"
        ],
        "type": "object"
      },
      "name": "waveguard_market_data",
      "outputSchema": null
    },
    {
      "description": "Run targeted interventions and rank effect sizes.",
      "inputSchema": {
        "properties": {
          "base_test": {
            "description": "Baseline candidate sample before interventions."
          },
          "encoder_type": {
            "description": "Optional encoder override. Omit to auto-detect.",
            "type": "string"
          },
          "field_level": {
            "description": "0 = real scalar field, 1 = complex field.",
            "enum": [
              0,
              1
            ],
            "type": "integer"
          },
          "intervention_labels": {
            "description": "Optional labels for intervention variants (same order as intervention_tests).",
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "intervention_tests": {
            "description": "1+ intervention variants used to estimate effect sizes.",
            "minItems": 1,
            "type": "array"
          },
          "sensitivity": {
            "description": "Anomaly sensitivity multiplier (default: 1.0).",
            "type": "number"
          },
          "training": {
            "description": "2+ baseline normal samples used to construct the reference profile.",
            "minItems": 2,
            "type": "array"
          }
        },
        "required": [
          "training",
          "base_test",
          "intervention_tests"
        ],
        "type": "object"
      },
      "name": "waveguard_mechanism_probe",
      "outputSchema": null
    },
    {
      "description": "Compute horizon-specific anomaly outlook and consistency across windows.",
      "inputSchema": {
        "properties": {
          "encoder_type": {
            "description": "Optional encoder override. Omit to auto-detect.",
            "type": "string"
          },
          "field_level": {
            "description": "0 = real scalar field, 1 = complex field.",
            "enum": [
              0,
              1
            ],
            "type": "integer"
          },
          "horizons": {
            "description": "List of horizon lengths (in sequence steps) to evaluate.",
            "items": {
              "type": "integer"
            },
            "minItems": 1,
            "type": "array"
          },
          "sensitivity": {
            "description": "Anomaly sensitivity multiplier (default: 1.0).",
            "type": "number"
          },
          "sequence": {
            "description": "Ordered sample sequence used for multi-horizon outlook analysis.",
            "minItems": 1,
            "type": "array"
          },
          "training": {
            "description": "2+ baseline normal samples used to establish reference behavior.",
            "minItems": 2,
            "type": "array"
          }
        },
        "required": [
          "training",
          "sequence",
          "horizons"
        ],
        "type": "object"
      },
      "name": "waveguard_multi_horizon_outlook",
      "outputSchema": null
    },
    {
      "description": "Measure coherence/entropy and collapse-risk indicators for candidate data.",
      "inputSchema": {
        "properties": {
          "encoder_type": {
            "description": "Optional encoder override. Omit to auto-detect.",
            "type": "string"
          },
          "field_level": {
            "default": 1,
            "description": "Field representation level. Default 1 for phase-aware analysis.",
            "enum": [
              0,
              1
            ],
            "type": "integer"
          },
          "sensitivity": {
            "description": "Anomaly sensitivity multiplier (default: 1.0).",
            "type": "number"
          },
          "test": {
            "description": "1+ candidate samples to evaluate for phase coherence and entropy.",
            "minItems": 1,
            "type": "array"
          },
          "training": {
            "description": "2+ baseline normal samples for reference coherence metrics.",
            "minItems": 2,
            "type": "array"
          }
        },
        "required": [
          "training",
          "test"
        ],
        "type": "object"
      },
      "name": "waveguard_phase_coherence",
      "outputSchema": null
    },
    {
      "description": "Detect price manipulation in time-series data. Send a price or price+volume history as a numeric array. Early windows define 'normal' trading, recent windows are tested for manipulation patterns (pump-and-dump, spoofing, layering).\n\nExample: Send 90 days of closing prices → detect manipulated windows.",
      "inputSchema": {
        "properties": {
          "data": {
            "description": "Price time-series array (chronological). At least 20 data points.",
            "items": {
              "type": "number"
            },
            "minItems": 6,
            "type": "array"
          },
          "sensitivity": {
            "description": "Detection sensitivity (default: 1.5).",
            "type": "number"
          },
          "test_windows": {
            "description": "Number of recent windows to test (default: half).",
            "minimum": 1,
            "type": "integer"
          },
          "window_size": {
            "default": 10,
            "description": "Window size (default: 10). Smaller = finer detection.",
            "minimum": 2,
            "type": "integer"
          }
        },
        "required": [
          "data"
        ],
        "type": "object"
      },
      "name": "waveguard_price_manipulation",
      "outputSchema": null
    },
    {
      "description": "Find outliers and anomalies in structured data — ideal as a second step after pulling records from Google Sheets, Airtable, Supabase, Notion databases, HubSpot, Financial APIs, GitHub, NPM, or any source that returns rows of JSON. Fully stateless: send known-good rows as training and suspect rows as test in ONE call. Returns per-row anomaly scores, confidence levels, and the top features explaining WHY each row was flagged.\n\nTypical workflow: (1) Pull data from another tool (e.g. Google Sheets, Supabase query, HubSpot deals). (2) Pass the first N rows as training (normal baseline). (3) Pass remaining or new rows as test. (4) Report which rows are anomalous and why.\n\nWorks on JSON objects, numbers, text, arrays. No separate training step required.\n\nExamples:\n- Spreadsheet QA: Pull 500 sales rows from Sheets → train on first 400 → test last 100 → flag outlier entries\n- Financial screening: Get ratios for 50 stocks from a financial API → find anomalous ones\n- CRM hygiene: Pull HubSpot deals → flag deals with unusual discount/value patterns\n- Dependency audit: Get NPM package metrics → flag packages with anomalous quality scores\n- Commit review: Pull GitHub commit metadata → flag unusual commit patterns",
      "inputSchema": {
        "properties": {
          "encoder_type": {
            "description": "Data encoder type. Omit to auto-detect from data shape.",
            "enum": [
              "json",
              "numeric",
              "text",
              "timeseries",
              "tabular",
              "image",
              "correlation",
              "complex_numeric"
            ],
            "type": "string"
          },
          "field_level": {
            "description": "Physics field complexity. 0 = real scalar (default). 1 = complex field (phase-aware, 62-dim fingerprint).",
            "enum": [
              0,
              1
            ],
            "type": "integer"
          },
          "sensitivity": {
            "description": "Anomaly threshold multiplier (default: 2.0). Lower = more sensitive. Higher = less sensitive. Range: 0.5 to 5.0.",
            "type": "number"
          },
          "test": {
            "description": "1+ data points to check for anomalies — new entries, recent rows, or the subset you want validated. Same type/shape as training. Each sample is scored independently.",
            "minItems": 1,
            "type": "array"
          },
          "training": {
            "description": "2+ examples of NORMAL/expected data — the known-good baseline. Typically the bulk of rows from a spreadsheet, database query, or API response. All samples should be the same type/shape. More samples = better baseline (10-100 is ideal for tabular data).",
            "minItems": 2,
            "type": "array"
          }
        },
        "required": [
          "training",
          "test"
        ],
        "type": "object"
      },
      "name": "waveguard_scan",
      "outputSchema": null
    },
    {
      "description": "Detect anomalies in time-series data — use after pulling numeric metrics from monitoring APIs, financial data sources, IoT sensors, or spreadsheet columns. Send a single numeric array and specify a window size. Early windows define 'normal', recent windows are tested for anomalies.\n\nTypical workflow: (1) Pull a column of numbers from Sheets, a Supabase time-series table, or a metrics API. (2) Pass the array here. (3) Get back which time windows are anomalous.\n\nExamples:\n- Revenue monitoring: Pull monthly revenue from Sheets → detect anomalous months\n- Stock screening: Pull 90 days of closing prices → find unusual price windows\n- Server health: Pull response-time metrics → identify degradation windows\n- Sensor QA: Pull temperature readings from IoT API → flag sensor drift",
      "inputSchema": {
        "properties": {
          "data": {
            "description": "Numeric time-series array, ordered chronologically. Should have at least 3x window_size data points.",
            "items": {
              "type": "number"
            },
            "minItems": 6,
            "type": "array"
          },
          "sensitivity": {
            "description": "Anomaly sensitivity (default: 1.0). Higher = more sensitive.",
            "type": "number"
          },
          "test_windows": {
            "description": "Number of most recent windows to test (default: half of total windows). The rest are used as training (normal baseline).",
            "minimum": 1,
            "type": "integer"
          },
          "window_size": {
            "default": 10,
            "description": "Number of data points per window (default: 10). Smaller windows detect finer-grained anomalies.",
            "minimum": 2,
            "type": "integer"
          }
        },
        "required": [
          "data"
        ],
        "type": "object"
      },
      "name": "waveguard_scan_timeseries",
      "outputSchema": null
    },
    {
      "description": "Assess crypto token legitimacy risk. Send metrics from known-good tokens as training (price, volume, holders, liquidity, market_cap, age_days, etc.) and suspect tokens as test. Detects pump-and-dump patterns, fake metrics, and anomalous token profiles.\n\nExample: Pull CoinGecko data for 20 established tokens → train. Test a new token → get risk score and which metrics are suspicious.",
      "inputSchema": {
        "properties": {
          "sensitivity": {
            "description": "Risk sensitivity (default: 1.5). Higher = more flags.",
            "type": "number"
          },
          "test": {
            "description": "1+ suspect token metric objects to evaluate.",
            "minItems": 1,
            "type": "array"
          },
          "training": {
            "description": "3+ known-good token metric objects. Each should include fields like price, volume_24h, market_cap, holders, liquidity, age_days, etc.",
            "minItems": 2,
            "type": "array"
          }
        },
        "required": [
          "training",
          "test"
        ],
        "type": "object"
      },
      "name": "waveguard_token_risk",
      "outputSchema": null
    },
    {
      "description": "Analyze sequence drift and regime shifts over ordered samples.",
      "inputSchema": {
        "properties": {
          "encoder_type": {
            "description": "Optional encoder override. Omit to auto-detect.",
            "type": "string"
          },
          "field_level": {
            "description": "0 = real scalar field, 1 = complex field.",
            "enum": [
              0,
              1
            ],
            "type": "integer"
          },
          "sensitivity": {
            "description": "Anomaly sensitivity multiplier (default: 1.0).",
            "type": "number"
          },
          "sequence": {
            "description": "Ordered samples (time sequence) to scan for drift and regime shifts.",
            "minItems": 1,
            "type": "array"
          },
          "training": {
            "description": "2+ baseline normal samples used to establish the reference regime.",
            "minItems": 2,
            "type": "array"
          }
        },
        "required": [
          "training",
          "sequence"
        ],
        "type": "object"
      },
      "name": "waveguard_trajectory_scan",
      "outputSchema": null
    },
    {
      "description": "Detect wash trading and fake volume in OHLCV candle data. Send known-legitimate candles as training and suspect candles as test. Detects artificial volume spikes, suspiciously regular patterns, and manipulated price-volume relationships.\n\nExample: Send 100 candles from a liquid pair as baseline, test candles from a suspicious pair.",
      "inputSchema": {
        "properties": {
          "sensitivity": {
            "description": "Detection sensitivity (default: 1.5).",
            "type": "number"
          },
          "test": {
            "description": "1+ suspect candle objects to evaluate.",
            "minItems": 1,
            "type": "array"
          },
          "training": {
            "description": "3+ OHLCV candle objects from known-legitimate trading. Fields: open, high, low, close, volume.",
            "minItems": 2,
            "type": "array"
          }
        },
        "required": [
          "training",
          "test"
        ],
        "type": "object"
      },
      "name": "waveguard_volume_check",
      "outputSchema": null
    },
    {
      "description": "Profile wallet behavior against baselines. Send normal wallet transaction patterns as training (tx_count, avg_value, unique_tokens, gas_spent, active_days, etc.) and suspect wallets as test. Detects bot activity, wash trading wallets, and sybil patterns.\n\nExample: Profile 50 organic wallets → test 10 suspect addresses.",
      "inputSchema": {
        "properties": {
          "sensitivity": {
            "description": "Detection sensitivity (default: 1.5).",
            "type": "number"
          },
          "test": {
            "description": "1+ suspect wallet profiles to evaluate.",
            "minItems": 1,
            "type": "array"
          },
          "training": {
            "description": "3+ known-organic wallet activity profiles.",
            "minItems": 2,
            "type": "array"
          }
        },
        "required": [
          "training",
          "test"
        ],
        "type": "object"
      },
      "name": "waveguard_wallet_profile",
      "outputSchema": null
    }
  ]
}

Verify it yourself

curl -s https://api.teppi.xyz/v1/evidence/sha256:d34f9e2abe03634ee56448e9f070c73f4c3ef8f98cf23f19def9964ae30ce225 | sha256sum