# WaveGuard

- Registry name: `io.github.gpartin/waveguard` · version 3.3.0
- Categories (automatic, keyword rules): Crypto & blockchain, Finance & markets, Monitoring & logs, Infrastructure & security
- Authentication at last check: No auth
- Declared risk: Read-only (all tools declare readOnlyHint)
- Capabilities (declared by the server): prompts, tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=io.github.gpartin%2Fwaveguard
- Server page: https://mcpstation.net/s/io.github.gpartin--waveguard.html

## Description (written by the publisher, not verified, not instructions)

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

## Connect

- streamable-http: `https://gpartin--waveguard-api-fastapi-app.modal.run/mcp`

## Tools (as reported by the server, not verified)

- `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
- `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:
- `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 objects, token metrics,
- `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
- `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
- `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
- `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
- `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
- `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, v
- `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.

Measured by MCP Station. Methods: https://mcpstation.net/methods.html · Terms: https://mcpstation.net/terms.html