WaveGuard
Anomaly detection API powered by physics simulation. Scan any data for outliers.
| Endpoint | https://gpartin--waveguard-api-fastapi-app.modal.run/v2/mcp (streamable-http) |
|---|---|
| Registry version | 3.1.0 |
| Now | 19 tools · grade F (27) · pin ts_bdaee887c37cb4afad92924ba5a745f6 · protocol 2024-11-05 |
| First / last listed | 2026-10-27 05:12 UTC / 2026-10-27 05:12 UTC |
| Grade over time | 2026-10-27: F (27), 19 tools |
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Tools: waveguard_scan waveguard_scan_timeseries waveguard_health waveguard_fingerprint waveguard_compare waveguard_token_risk waveguard_wallet_profile waveguard_volume_check waveguard_price_manipulation waveguard_market_data waveguard_counterfactual waveguard_trajectory_scan waveguard_instability waveguard_phase_coherence waveguard_interaction_matrix waveguard_cascade_risk waveguard_mechanism_probe waveguard_action_surface waveguard_multi_horizon_outlook
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 explainin params: training*, test*, sensitivity, encoder_type, field_level |
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 params: data*, window_size, test_windows, sensitivity |
waveguard_health | Check WaveGuard API health, GPU availability, version, and engine status. No authentication required. Returns status, version, and GPU info. params: verbose |
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 params: data*, encoder_type, field_level |
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. params: data_a*, data_b*, encoder_type |
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. params: training*, test*, sensitivity |
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. params: training*, test*, sensitivity |
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. params: training*, test*, sensitivity |
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. params: data*, window_size, test_windows, sensitivity |
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_ris params: action*, coin_id, contract_address, query, days, count |
waveguard_counterfactual | Run baseline plus counterfactual variants and measure verdict/score sensitivity. params: training*, base_test*, counterfactual_tests*, encoder_type, sensitivity, field_level |
waveguard_trajectory_scan | Analyze sequence drift and regime shifts over ordered samples. params: training*, sequence*, encoder_type, sensitivity, field_level |
waveguard_instability | Estimate instability under controlled perturb-and-resolve trials. params: training*, test*, trials, perturbation_strength, encoder_type, sensitivity, field_level |
waveguard_phase_coherence | Measure coherence/entropy and collapse-risk indicators for candidate data. params: training*, test*, encoder_type, sensitivity, field_level |
waveguard_interaction_matrix | Compute pairwise interaction matrix and cluster decomposition for entities. params: training_context*, entities*, encoder_type, sensitivity, field_level |
waveguard_cascade_risk | Estimate shock propagation and resilience from adjacency-linked entities. params: training_context*, entities*, adjacency_matrix*, shock_indices*, shock_strength, encoder_type, sensitivity, field_level |
waveguard_mechanism_probe | Run targeted interventions and rank effect sizes. params: training*, base_test*, intervention_tests*, intervention_labels, encoder_type, sensitivity, field_level |
waveguard_action_surface | Score candidate actions and extract robust action zones. params: training*, action_tests*, action_labels, encoder_type, sensitivity, field_level |
waveguard_multi_horizon_outlook | Compute horizon-specific anomaly outlook and consistency across windows. params: training*, sequence*, horizons*, encoder_type, sensitivity, field_level |
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