Summary
A physics-based anomaly detector that runs GPU simulations server-side so you don't need ML expertise or training pipelines. Point it at server metrics, financial transactions, IoT sensors, or time series and get back anomaly scores with per-feature explanations in one stateless API call. The MCP server wraps the WaveGuard API deployed on Modal, exposing scan operations through streamable HTTP. Under the hood it encodes your data onto a 64³ lattice and runs coupled wave equations to spot outliers. Benchmarks show it catching 7 out of 7 historical crypto crashes with 27 days average lead time. Useful when alert fatigue matters more than perfect recall, or when your schema changes too often for traditional model retraining.
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Public tool metadata for what this MCP can expose to an agent.
19 tools
waveguard_scanFind 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 train...5 params
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 train...
Parameters* required
encoder_typestringData encoder type. Omit to auto-detect from data shape.one of
json · numeric · text · timeseries · tabular · imagefield_levelintegerPhysics field complexity. 0 = real scalar (default). 1 = complex field (phase-aware, 62-dim fingerprint).one of
0 · 1sensitivitynumberAnomaly threshold multiplier (default: 2.0). Lower = more sensitive. Higher = less sensitive. Range: 0.5 to 5.0.
testarray1+ data points to check for anomalies — new entries, recent rows, or the subset you want validated. Same type/shape as training. Each sample is scored independently.
trainingarray2+ examples of NORMAL/expected data — the known-good baseline. Typically the bulk of rows from a spreadsheet, database query, or API response. All samples should be the same type/shape. More sample...
waveguard_scan_timeseriesDetect 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 anoma...4 params
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 anoma...
Parameters* required
dataarrayNumeric time-series array, ordered chronologically. Should have at least 3x window_size data points.
sensitivitynumberAnomaly sensitivity (default: 1.0). Higher = more sensitive.
test_windowsintegerNumber of most recent windows to test (default: half of total windows). The rest are used as training (normal baseline).
window_sizeintegerNumber of data points per window (default: 10). Smaller windows detect finer-grained anomalies.default: 10
waveguard_healthCheck WaveGuard API health, GPU availability, version, and engine status. No authentication required. Returns status, version, and GPU info.1 params
Check WaveGuard API health, GPU availability, version, and engine status. No authentication required. Returns status, version, and GPU info.
Parameters* required
verbosebooleanReturn detailed health info including memory and uptime (default: false).default: false
waveguard_fingerprintGet 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...3 params
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...
Parameters* required
datavalueAny data item to fingerprint: JSON object, numeric array, string, or structured record.
encoder_typestringData encoder. Omit to auto-detect.one of
json · numeric · text · timeseries · tabular · complex_numericfield_levelinteger0 = real scalar 52-dim (default), 1 = complex field 62-dim.one of
0 · 1waveguard_compareCompare 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. Co...3 params
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. Co...
Parameters* required
data_avalueFirst data item to compare.
data_bvalueSecond data item to compare (same type as data_a).
encoder_typestringData encoder. Omit to auto-detect.one of
json · numeric · text · timeseries · tabularwaveguard_token_riskAssess 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...3 params
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...
Parameters* required
sensitivitynumberRisk sensitivity (default: 1.5). Higher = more flags.
testarray1+ suspect token metric objects to evaluate.
trainingarray3+ known-good token metric objects. Each should include fields like price, volume_24h, market_cap, holders, liquidity, age_days, etc.
waveguard_wallet_profileProfile 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 or...3 params
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 or...
Parameters* required
sensitivitynumberDetection sensitivity (default: 1.5).
testarray1+ suspect wallet profiles to evaluate.
trainingarray3+ known-organic wallet activity profiles.
waveguard_volume_checkDetect 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...3 params
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...
Parameters* required
sensitivitynumberDetection sensitivity (default: 1.5).
testarray1+ suspect candle objects to evaluate.
trainingarray3+ OHLCV candle objects from known-legitimate trading. Fields: open, high, low, close, volume.
waveguard_price_manipulationDetect 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 → det...4 params
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 → det...
Parameters* required
dataarrayPrice time-series array (chronological). At least 20 data points.
sensitivitynumberDetection sensitivity (default: 1.5).
test_windowsintegerNumber of recent windows to test (default: half).
window_sizeintegerWindow size (default: 10). Smaller = finer detection.default: 10
waveguard_market_dataFetch 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/sy...6 params
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/sy...
Parameters* required
actionstringWhat data to fetch:
- token_data: full metrics for a CoinGecko coin
- price_history: daily prices (for price_manipulation)
- ohlc: OHLC candles (for volume_check)
- top_coins: top N by market cap (...one of
token_data · price_history · ohlc · top_coins · search · dex_tokencoin_idstringCoinGecko coin ID (e.g. 'bitcoin', 'ethereum'). Required for token_data, price_history, ohlc.
contract_addressstringToken contract address (any chain). Required for dex_token.
countintegerNumber of results for top_coins (default: 25).default: 25
daysintegerNumber of days of history (default: 90 for price_history, 30 for ohlc).default: 90
querystringSearch query. Required for search, dex_search.
waveguard_counterfactualRun baseline plus counterfactual variants and measure verdict/score sensitivity.6 params
Run baseline plus counterfactual variants and measure verdict/score sensitivity.
Parameters* required
base_testvaluecounterfactual_testsarrayencoder_typestringfield_levelintegerone of
0 · 1sensitivitynumbertrainingarraywaveguard_trajectory_scanAnalyze sequence drift and regime shifts over ordered samples.5 params
Analyze sequence drift and regime shifts over ordered samples.
Parameters* required
encoder_typestringfield_levelintegerone of
0 · 1sensitivitynumbersequencearraytrainingarraywaveguard_instabilityEstimate instability under controlled perturb-and-resolve trials.7 params
Estimate instability under controlled perturb-and-resolve trials.
Parameters* required
encoder_typestringfield_levelintegerone of
0 · 1perturbation_strengthnumberdefault: 0.02
sensitivitynumbertestarraytrainingarraytrialsintegerdefault: 12
waveguard_phase_coherenceMeasure coherence/entropy and collapse-risk indicators for candidate data.5 params
Measure coherence/entropy and collapse-risk indicators for candidate data.
Parameters* required
encoder_typestringfield_levelintegerone of
0 · 1default: 1sensitivitynumbertestarraytrainingarraywaveguard_interaction_matrixCompute pairwise interaction matrix and cluster decomposition for entities.5 params
Compute pairwise interaction matrix and cluster decomposition for entities.
Parameters* required
encoder_typestringentitiesarrayfield_levelintegerone of
0 · 1default: 1sensitivitynumbertraining_contextarraywaveguard_cascade_riskEstimate shock propagation and resilience from adjacency-linked entities.8 params
Estimate shock propagation and resilience from adjacency-linked entities.
Parameters* required
adjacency_matrixarrayencoder_typestringentitiesarrayfield_levelintegerone of
0 · 1default: 1sensitivitynumbershock_indicesarrayshock_strengthnumberdefault: 0.05
training_contextarraywaveguard_mechanism_probeRun targeted interventions and rank effect sizes.7 params
Run targeted interventions and rank effect sizes.
Parameters* required
base_testvalueencoder_typestringfield_levelintegerone of
0 · 1intervention_labelsarrayintervention_testsarraysensitivitynumbertrainingarraywaveguard_action_surfaceScore candidate actions and extract robust action zones.6 params
Score candidate actions and extract robust action zones.
Parameters* required
action_labelsarrayaction_testsarrayencoder_typestringfield_levelintegerone of
0 · 1sensitivitynumbertrainingarraywaveguard_multi_horizon_outlookCompute horizon-specific anomaly outlook and consistency across windows.6 params
Compute horizon-specific anomaly outlook and consistency across windows.
Parameters* required
encoder_typestringfield_levelintegerone of
0 · 1horizonsarraysensitivitynumbersequencearraytrainingarrayFeatured
Plug Mailtrap into your AI workflow and let it handle the email.
Connect Mailtrap MCP →Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →Make coding agent sessions - Searchable, Shareable, Vendor-neutral & Scored.
Try For Free →Your agent targets a perfect 10 Code Health score. Deterministic. Every commit.
Try For Free →Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →belt cli automatically finds the best tools and skills for your agent. image, video, music, tts...
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