io.github.gpartin/waveguard

down checked 2026-11-10T02:37:50.119Z

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Publisher-provided title

WaveGuard

Publisher-provided description

Anomaly detection API powered by physics simulation. Scan any data for outliers.

Summary by lookupd

Detects anomalies, outliers, wash trading, and token or wallet risk in structured, time-series, and crypto market data via physics-based…

Install

Claude Code, remote (streamable-http)

claude mcp add --transport http waveguard https://gpartin--waveguard-api-fastapi-app.modal.run/mcp

Codex, remote (streamable-http)

codex mcp add waveguard --url https://gpartin--waveguard-api-fastapi-app.modal.run/mcp

Cursor, remote (streamable-http) → .cursor/mcp.json

{
  "mcpServers": {
    "waveguard": {
      "url": "https://gpartin--waveguard-api-fastapi-app.modal.run/mcp"
    }
  }
}

Other clients: docs. Or tell your agent: Set up lookupd for me: read https://lookupd.com/agent.md and follow it.

Tools (19 tools)

Publisher-provided text

• waveguard_scan Find outliers and anomalies in structured data — ideal as a second step after pulling records from Google Sheets, Airtable, Supabase, Notion databases, HubSpot,
• waveguard_scan_timeseries Detect anomalies in time-series data — use after pulling numeric metrics from monitoring APIs, financial data sources, IoT sensors, or spreadsheet columns. Send
• waveguard_health Check WaveGuard API health, GPU availability, version, and engine status. No authentication required. Returns status, version, and GPU info.
• waveguard_fingerprint 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-e
• waveguard_compare Compare two data items for structural similarity using physics-based fingerprints. Returns cosine similarity (0–1) and Euclidean distance. Use for duplicate det
• waveguard_token_risk Assess crypto token legitimacy risk. Send metrics from known-good tokens as training (price, volume, holders, liquidity, market_cap, age_days, etc.) and suspect
• waveguard_wallet_profile Profile wallet behavior against baselines. Send normal wallet transaction patterns as training (tx_count, avg_value, unique_tokens, gas_spent, active_days, etc.
• waveguard_volume_check Detect wash trading and fake volume in OHLCV candle data. Send known-legitimate candles as training and suspect candles as test. Detects artificial volume spike
• waveguard_price_manipulation 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 ar
• waveguard_market_data Fetch live crypto market data from CoinGecko and DexScreener. No external data needed — WaveGuard pulls it for you. Use 'coin_id' for CoinGecko (e.g. 'bitcoin'
• waveguard_counterfactual Run baseline plus counterfactual variants and measure verdict/score sensitivity.
• waveguard_trajectory_scan Analyze sequence drift and regime shifts over ordered samples.
• waveguard_instability Estimate instability under controlled perturb-and-resolve trials.
• waveguard_phase_coherence Measure coherence/entropy and collapse-risk indicators for candidate data.
• waveguard_interaction_matrix Compute pairwise interaction matrix and cluster decomposition for entities.
• waveguard_cascade_risk Estimate shock propagation and resilience from adjacency-linked entities.
• waveguard_mechanism_probe Run targeted interventions and rank effect sizes.
• waveguard_action_surface Score candidate actions and extract robust action zones.
• waveguard_multi_horizon_outlook Compute horizon-specific anomaly outlook and consistency across windows.

Details

• Version: 3.3.0
• Transports: streamable-http
• Uptime (7 days): 30%
• Median latency: 9787 ms
• First indexed: 2026-11-01T09:31:43.624Z
• Last seen in registry: 2026-11-01T09:31:43.668Z
• Repository: https://github.com/gpartin/LFMAnomalyDetection
• Website: https://github.com/gpartin/WaveGuardClient

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