Anomaly Detector Client MCP Server

The Anomaly Detector Client API is a powerful machine learning service designed to automatically identify anomalies, outliers, and significant change points within time series datasets. Developed to serve both enterprise and developer ecosystems, this API provides intelligent pattern recognition capabilities that would otherwise require extensive data science expertise to implement from scratch. The service supports two operational modes: stateless mode, which analyzes complete datasets in a single request without retaining context between calls, and stateful mode, which maintains session state for continuous monitoring and iterative detection. In stateless mode, three distinct functionalities are available. The Entire Detect endpoint processes an entire time series to identify all anomalies within the dataset using a model trained on the provided data. The Change Point Detection endpoint identifies moments where the statistical properties of the data undergo significant shifts. The Last Point Detection endpoint efficiently analyzes only the most recent data point against historical context, making it ideal for real-time monitoring scenarios. Common use cases span multiple industries, including financial transaction monitoring for fraudulent activities, infrastructure health monitoring for server metrics and IoT sensor data, supply chain analytics for inventory and demand fluctuations, and application performance monitoring where sudden deviations in response times or error rates require immediate attention.