WaveGuard

io.github.gpartin/waveguardVersion 3.3.0

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

Status

Responds2627 msNov 14, 2026, 14:52 UTCAnswered our handshake without sign-in.

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Last checked: Nov 14, 2026, 14:52 UTC
Latency: 2627 ms
Remotes responding: 1 of 1
Sign-in: No sign-in
Hosting: Hosted outside the EU (detected)
Registry data as of: Nov 14, 2026, 21:12 UTC

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Connect

Streamable HTTP

https://gpartin--waveguard-api-fastapi-app.modal.run/mcpResponds2627 msNov 14, 2026, 14:52 UTC

No sign-in

Claude Code

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

Claude Desktop and claude.ai

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Settings > Connectors > Add custom connector
Name: waveguard
Remote MCP server URL: https://gpartin--waveguard-api-fastapi-app.modal.run/mcp

Cursor

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

VS Code

code --add-mcp '{"name":"waveguard","type":"http","url":"https://gpartin--waveguard-api-fastapi-app.modal.run/mcp"}'

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Tools

19 tools

Tools of WaveGuard
	Description		Read-only	Idempotent	Open world
`waveguard_action_surface`Score candidate actions and extract robust action zones.	Score candidate actions and extract robust action zones.	No	Yes	Yes	Yes
`waveguard_cascade_risk`Estimate shock propagation and resilience from adjacency-linked entities.	Estimate shock propagation and resilience from adjacency-linked entities.	No	Yes	Yes	Yes
`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. Cosine similarity > 0.95 = very similar. < 0.80 = structurally different.	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. Cosine similarity > 0.95 = very similar. < 0.80 = structurally different.	No	Yes	Yes	Yes
`waveguard_counterfactual`Run baseline plus counterfactual variants and measure verdict/score sensitivity.	Run baseline plus counterfactual variants and measure verdict/score sensitivity.	No	Yes	Yes	Yes
`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 objects, token metrics, wallet activity, trading data, or any structured data. Returns a deterministic vector with labeled dimensions (chi statistics, energy distribution, gradient patterns, and phase coherence at Level 1).	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. Returns a deterministic vector with labeled dimensions (chi statistics, energy distribution, gradient patterns, and phase coherence at Level 1).	No	Yes	Yes	Yes
`waveguard_health`Check WaveGuard API health, GPU availability, version, and engine status. No authentication required. Returns status, version, and GPU info.	Check WaveGuard API health, GPU availability, version, and engine status. No authentication required. Returns status, version, and GPU info.	No	Yes	Yes	No
`waveguard_instability`Estimate instability under controlled perturb-and-resolve trials.	Estimate instability under controlled perturb-and-resolve trials.	No	Yes	Yes	Yes
`waveguard_interaction_matrix`Compute pairwise interaction matrix and cluster decomposition for entities.	Compute pairwise interaction matrix and cluster decomposition for entities.	No	Yes	Yes	Yes
`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 name/symbol. Returns: price, volume, market cap, liquidity, price history, OHLC candles — ready to feed into waveguard_token_risk, waveguard_volume_check, or waveguard_price_manipulation.	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 name/symbol. Returns: price, volume, market cap, liquidity, price history, OHLC candles — ready to feed into waveguard_token_risk, waveguard_volume_check, or waveguard_price_manipulation.	No	Yes	Yes	Yes
`waveguard_mechanism_probe`Run targeted interventions and rank effect sizes.	Run targeted interventions and rank effect sizes.	No	Yes	Yes	Yes
`waveguard_multi_horizon_outlook`Compute horizon-specific anomaly outlook and consistency across windows.	Compute horizon-specific anomaly outlook and consistency across windows.	No	Yes	Yes	Yes
`waveguard_phase_coherence`Measure coherence/entropy and collapse-risk indicators for candidate data.	Measure coherence/entropy and collapse-risk indicators for candidate data.	No	Yes	Yes	Yes
`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 → detect manipulated windows.	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 → detect manipulated windows.	No	Yes	Yes	Yes
`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 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. Typical 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. Works on JSON objects, numbers, text, arrays. No separate training step required. Examples: - Spreadsheet QA: Pull 500 sales rows from Sheets → train on first 400 → test last 100 → flag outlier entries - Financial screening: Get ratios for 50 stocks from a financial API → find anomalous ones - CRM hygiene: Pull HubSpot deals → flag deals with unusual discount/value patterns - Dependency audit: Get NPM package metrics → flag packages with anomalous quality scores - Commit review: Pull GitHub commit metadata → flag unusual commit patterns	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. Typical 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. Works on JSON objects, numbers, text, arrays. No separate training step required. Examples: - Spreadsheet QA: Pull 500 sales rows from Sheets → train on first 400 → test last 100 → flag outlier entries - Financial screening: Get ratios for 50 stocks from a financial API → find anomalous ones - CRM hygiene: Pull HubSpot deals → flag deals with unusual discount/value patterns - Dependency audit: Get NPM package metrics → flag packages with anomalous quality scores - Commit review: Pull GitHub commit metadata → flag unusual commit patterns	No	Yes	Yes	Yes
`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 anomalies. Typical 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. Examples: - Revenue monitoring: Pull monthly revenue from Sheets → detect anomalous months - Stock screening: Pull 90 days of closing prices → find unusual price windows - Server health: Pull response-time metrics → identify degradation windows - Sensor QA: Pull temperature readings from IoT API → flag sensor drift	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. Typical 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. Examples: - Revenue monitoring: Pull monthly revenue from Sheets → detect anomalous months - Stock screening: Pull 90 days of closing prices → find unusual price windows - Server health: Pull response-time metrics → identify degradation windows - Sensor QA: Pull temperature readings from IoT API → flag sensor drift	No	Yes	Yes	Yes
`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 data for 20 established tokens → train. Test a new token → get risk score and which metrics are suspicious.	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 data for 20 established tokens → train. Test a new token → get risk score and which metrics are suspicious.	No	Yes	Yes	Yes
`waveguard_trajectory_scan`Analyze sequence drift and regime shifts over ordered samples.	Analyze sequence drift and regime shifts over ordered samples.	No	Yes	Yes	Yes
`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 pair as baseline, test candles from a suspicious pair.	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 pair as baseline, test candles from a suspicious pair.	No	Yes	Yes	Yes
`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 organic wallets → test 10 suspect addresses.	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 organic wallets → test 10 suspect addresses.	No	Yes	Yes	Yes

Details

Registry status: Active
Published: Mar 1, 2026
Updated: Mar 1, 2026
Website: https://github.com/gpartin/WaveGuardClient⁠ (opens in a new tab)
Repository: https://github.com/gpartin/LFMAnomalyDetection⁠ (opens in a new tab)
Categories (suggested): Finance and paymentsSuggested categories are assigned automatically from the registry text and may be wrong. Verified operators can correct them.
GDPR: Not declared

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