Skip to main content
Glama

find_anomalies

Detect data anomalies using statistical, pattern, and missing value analysis methods to identify outliers and irregularities in datasets.

Instructions

Find anomalies in the data using multiple detection methods.

Returns: FindAnomaliesResult with comprehensive anomaly detection results

Input Schema

NameRequiredDescriptionDefault
columnsNoList of columns to analyze (None = all columns)
sensitivityNoSensitivity threshold for anomaly detection (0-1)
methodsNoDetection methods to use (None = all methods)

Input Schema (JSON Schema)

{ "properties": { "columns": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "List of columns to analyze (None = all columns)" }, "methods": { "anyOf": [ { "items": { "enum": [ "statistical", "pattern", "missing" ], "type": "string" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Detection methods to use (None = all methods)" }, "sensitivity": { "default": 0.95, "description": "Sensitivity threshold for anomaly detection (0-1)", "type": "number" } }, "type": "object" }

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jonpspri/databeak'

If you have feedback or need assistance with the MCP directory API, please join our Discord server