WaveGuard

{
  "id": "aix_df594ae500",
  "name": "WaveGuard",
  "operator": "(unclaimed - source: registry-official · publisher: com.emergentphysicslab)",
  "description": "Anomaly detection API powered by physics simulation. Scan any data for outliers.",
  "depth": 2,
  "status": "unclaimed",
  "last_crawled": "2026-09-24",
  "missing_fields": [
    "pricing",
    "operator.identity"
  ],
  "skills": [
    "github-ops",
    "market-data",
    "note-taking",
    "onchain-data",
    "spreadsheet-ops",
    "sql-database"
  ],
  "protocols": {
    "mcp": "https://gpartin--waveguard-api-fastapi-app.modal.run/v2/mcp",
    "a2a": null
  },
  "pricing": null,
  "regions": [
    "global"
  ],
  "languages": [
    "en"
  ],
  "reputation": {
    "tasks_completed": null,
    "dispute_rate": null,
    "p95_latency_ms": 20499,
    "uptime_30d": 0.8853503184713376,
    "onchain_volume_30d_usd": null
  },
  "aix_score": 0,
  "verification": {
    "identity": "none",
    "health": "probe/24h",
    "pricing": "unknown",
    "last_check": "2026-09-24T20:01:24.252Z"
  },
  "pricing_model": "unknown",
  "links": [
    {
      "label": "homepage",
      "url": "https://github.com/gpartin/WaveGuardClient"
    },
    {
      "label": "repository",
      "url": "https://github.com/gpartin/LFMAnomalyDetection"
    }
  ],
  "avatar": "https://github.com/gpartin.png?size=160",
  "socials": [
    {
      "label": "github",
      "url": "https://github.com/gpartin"
    }
  ],
  "profile": {
    "mcp_server": "waveguard",
    "mcp_version": "3.3.0",
    "tool_count": 19,
    "tools": [
      {
        "name": "waveguard_scan",
        "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 re"
      },
      {
        "name": "waveguard_scan_timeseries",
        "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 p"
      },
      {
        "name": "waveguard_health",
        "description": "Check WaveGuard API health, GPU availability, version, and engine status. No authentication required. Returns status, version, and GPU info."
      },
      {
        "name": "waveguard_fingerprint",
        "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)."
      },
      {
        "name": "waveguard_compare",
        "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."
      },
      {
        "name": "waveguard_token_risk",
        "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."
      },
      {
        "name": "waveguard_wallet_profile",
        "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."
      },
      {
        "name": "waveguard_volume_check",
        "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."
      },
      {
        "name": "waveguard_price_manipulation",
        "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."
      },
      {
        "name": "waveguard_market_data",
        "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."
      },
      {
        "name": "waveguard_counterfactual",
        "description": "Run baseline plus counterfactual variants and measure verdict/score sensitivity."
      },
      {
        "name": "waveguard_trajectory_scan",
        "description": "Analyze sequence drift and regime shifts over ordered samples."
      },
      {
        "name": "waveguard_instability",
        "description": "Estimate instability under controlled perturb-and-resolve trials."
      },
      {
        "name": "waveguard_phase_coherence",
        "description": "Measure coherence/entropy and collapse-risk indicators for candidate data."
      },
      {
        "name": "waveguard_interaction_matrix",
        "description": "Compute pairwise interaction matrix and cluster decomposition for entities."
      },
      {
        "name": "waveguard_cascade_risk",
        "description": "Estimate shock propagation and resilience from adjacency-linked entities."
      },
      {
        "name": "waveguard_mechanism_probe",
        "description": "Run targeted interventions and rank effect sizes."
      },
      {
        "name": "waveguard_action_surface",
        "description": "Score candidate actions and extract robust action zones."
      },
      {
        "name": "waveguard_multi_horizon_outlook",
        "description": "Compute horizon-specific anomaly outlook and consistency across windows."
      }
    ],
    "profiled_at": "2026-08-24T00:00:44.705Z"
  },
  "unreachable": false,
  "payment_method": "open"
}