mcp server
WaveGuard
Anomaly detection API powered by physics simulation. Scan any data for outliers.
Description as published by the maintainer. Source
- version 3.1.0
- archived
archived — The linked repository returns 404. It was deleted, renamed or made private.
What this server can do
19 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.
waveguard_action_surface(training, field_level, sensitivity, action_tests, encoder_type, action_labels)- Score candidate actions and extract robust action zones. Required: training, action_tests.
waveguard_cascade_risk(entities, field_level, sensitivity, encoder_type, shock_indices, shock_strength, adjacency_matrix, training_context)- Estimate shock propagation and resilience from adjacency-linked entities. Required: training_context, entities, adjacency_matrix, shock_indices.
waveguard_compare(data_a, data_b, encoder_type)- 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. Required: data_a, data_b.
waveguard_counterfactual(training, base_test, field_level, sensitivity, encoder_type, counterfactual_tests)- Run baseline plus counterfactual variants and measure verdict/score sensitivity. Required: training, base_test, counterfactual_tests.
waveguard_fingerprint(data, field_level, encoder_type)- 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). Required: data.
waveguard_health(verbose)- Check WaveGuard API health, GPU availability, version, and engine status. No authentication required. Returns status, version, and GPU info.
waveguard_instability(test, trials, training, field_level, sensitivity, encoder_type, perturbation_strength)- Estimate instability under controlled perturb-and-resolve trials. Required: training, test.
waveguard_interaction_matrix(entities, field_level, sensitivity, encoder_type, training_context)- Compute pairwise interaction matrix and cluster decomposition for entities. Required: training_context, entities.
waveguard_market_data(days, count, query, action, coin_id, contract_address)- 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. Required: action.
waveguard_mechanism_probe(training, base_test, field_level, sensitivity, encoder_type, intervention_tests, intervention_labels)- Run targeted interventions and rank effect sizes. Required: training, base_test, intervention_tests.
waveguard_multi_horizon_outlook(horizons, sequence, training, field_level, sensitivity, encoder_type)- Compute horizon-specific anomaly outlook and consistency across windows. Required: training, sequence, horizons.
waveguard_phase_coherence(test, training, field_level, sensitivity, encoder_type)- Measure coherence/entropy and collapse-risk indicators for candidate data. Required: training, test.
waveguard_price_manipulation(data, sensitivity, window_size, test_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. Required: data.
waveguard_scan(test, training, field_level, sensitivity, encoder_type)- 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 Required: training, test.
waveguard_scan_timeseries(data, sensitivity, window_size, test_windows)- 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 Required: data.
waveguard_token_risk(test, training, sensitivity)- 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. Required: training, test.
waveguard_trajectory_scan(sequence, training, field_level, sensitivity, encoder_type)- Analyze sequence drift and regime shifts over ordered samples. Required: training, sequence.
waveguard_volume_check(test, training, sensitivity)- 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. Required: training, test.
waveguard_wallet_profile(test, training, sensitivity)- 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. Required: training, test.
Last successful function declaration observed on . Source: https://gpartin--waveguard-api-fastapi-app.modal.run/v2/mcp. We list what the server declared; we do not call any of these functions.
Endpoint status observed on . Source: https://gpartin--waveguard-api-fastapi-app.modal.run/v2/mcp.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| Latest published version | 3.1.0 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-02-26 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| First listed in the MCP Registry | 2026-02-26 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | not_found | GitHub returned 404 for the repository the maintainer listed. The project was deleted, renamed or made private, so the listing points at nothing. | as of fetch | GitHub | |
| mcp tools declared | 19 tools | Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. | as of probe | gpartin--waveguard-api-fastapi-app.modal.run | |
| mcp endpoint status | ok | The server listed 19 functions when asked. | as of probe | gpartin--waveguard-api-fastapi-app.modal.run |
Where to get it
Also from gpartin
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WaveGuard
— repository gone
Anomaly detection API powered by physics simulation. Scan any data for outliers.
This record as data
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