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waveguard_scan

Active

Tool of WaveGuard

declared in 3.3.0

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

Parameters schema

{
  "type": "object",
  "required": [
    "training",
    "test"
  ],
  "properties": {
    "test": {
      "type": "array",
      "minItems": 1,
      "description": "1+ 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."
    },
    "training": {
      "type": "array",
      "minItems": 2,
      "description": "2+ 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 samples = better baseline (10-100 is ideal for tabular data)."
    },
    "field_level": {
      "enum": [
        0,
        1
      ],
      "type": "integer",
      "description": "Physics field complexity. 0 = real scalar (default). 1 = complex field (phase-aware, 62-dim fingerprint)."
    },
    "sensitivity": {
      "type": "number",
      "description": "Anomaly threshold multiplier (default: 2.0). Lower = more sensitive. Higher = less sensitive. Range: 0.5 to 5.0."
    },
    "encoder_type": {
      "enum": [
        "json",
        "numeric",
        "text",
        "timeseries",
        "tabular",
        "image",
        "correlation",
        "complex_numeric"
      ],
      "type": "string",
      "description": "Data encoder type. Omit to auto-detect from data shape."
    }
  }
}

What this tool wraps· 5 endpoints

min confidence0.700.50

Parent server

WaveGuard

https://github.com/gpartin/LFMAnomalyDetection

2/7 registries
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