MCP server

WaveGuard

com.emergentphysicslab/waveguard

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

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

Working at last checkVerified publisher: emergentphysicslab.comAbout 5,527 tokens per request once connected19 tools

Website Source

https://gpartin--waveguard-api-fastapi-app.modal.run/v2/mcp

At a glance

Type: MCP server
Publisher: emergentphysicslab.com (verified by the MCP Registry)
Status: Working at last check
Tools: 19
Token cost: About 5,527 tokens per request
Sets up in: Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok
Last checked: 26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to Cursor Add to VS Code VS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http waveguard https://gpartin--waveguard-api-fastapi-app.modal.run/v2/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.waveguard]
url = "https://gpartin--waveguard-api-fastapi-app.modal.run/v2/mcp"

Gemini CLI

{"mcpServers":{"waveguard":{"httpUrl":"https://gpartin--waveguard-api-fastapi-app.modal.run/v2/mcp"}}}

Cursor

{"mcpServers":{"waveguard":{"url":"https://gpartin--waveguard-api-fastapi-app.modal.run/v2/mcp"}}}

VS Code

{"servers":{"waveguard":{"type":"http","url":"https://gpartin--waveguard-api-fastapi-app.modal.run/v2/mcp"}}}

Antigravity

agy mcp add waveguard https://gpartin--waveguard-api-fastapi-app.modal.run/v2/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"waveguard","server_url":"https://gpartin--waveguard-api-fastapi-app.modal.run/v2/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"waveguard","server_url":"https://gpartin--waveguard-api-fastapi-app.modal.run/v2/mcp"}

Tools

waveguard_action_surface ~243 tokens
Score candidate actions and extract robust action zones.
waveguard_cascade_risk ~359 tokens
Estimate shock propagation and resilience from adjacency-linked entities.
waveguard_compare ~214 tokens
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_counterfactual ~281 tokens
Run baseline plus counterfactual variants and measure verdict/score sensitivity.
waveguard_fingerprint ~290 tokens
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_health ~114 tokens
Check WaveGuard API health, GPU availability, version, and engine status. No authentication required. Returns status, version, and GPU info.
waveguard_instability ~275 tokens
Estimate instability under controlled perturb-and-resolve trials.
waveguard_interaction_matrix ~226 tokens
Compute pairwise interaction matrix and cluster decomposition for entities.
waveguard_market_data ~463 tokens
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,
waveguard_mechanism_probe ~283 tokens
Run targeted interventions and rank effect sizes.
waveguard_multi_horizon_outlook ~259 tokens
Compute horizon-specific anomaly outlook and consistency across windows.
waveguard_phase_coherence ~221 tokens
Measure coherence/entropy and collapse-risk indicators for candidate data.
waveguard_price_manipulation ~257 tokens
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 window
waveguard_scan ~689 tokens
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 ~444 tokens
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_token_risk ~259 tokens
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 establishe
waveguard_trajectory_scan ~213 tokens
Analyze sequence drift and regime shifts over ordered samples.
waveguard_volume_check ~227 tokens
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, tes
waveguard_wallet_profile ~210 tokens
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 1

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