WaveGuard

Reliability

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How to connect

Add it to any MCP client that supports remote servers.

{
  "mcpServers": {
    "waveguard-2": {
      "type": "http",
      "url": "https://gpartin--waveguard-api-fastapi-app.modal.run/mcp"
    }
  }
}

19 tools

• 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, Financial APIs, GitHub, NPM, or any source that returns rows of JSON. Fully stateless: send known-good rows as…

• 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 a single numeric array and specify a window size. Early windows define 'normal', recent windows are tested for…

• 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-equation dynamics — useful for similarity search, clustering, baseline comparison, and drift detection. Works on JSON…

• waveguard_compare

  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…

• 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 tokens as test. Detects pump-and-dump patterns, fake metrics, and anomalous token profiles. Example: Pull CoinGecko…

• 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.) and suspect wallets as test. Detects bot activity, wash trading wallets, and sybil patterns. Example: Profile 50…

• 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 spikes, suspiciously regular patterns, and manipulated price-volume relationships. Example: Send 100 candles from a liquid…

• 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 are tested for manipulation patterns (pump-and-dump, spoofing, layering). Example: Send 90 days of closing prices →…

• 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', 'ethereum', 'solana'). Use 'contract_address' for DexScreener (any chain). Use 'search' to find token IDs by…

• 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.

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