Anomaly Finder Client MCP Server

The Anomaly Finder Client API is a robust, stateful service designed for proactive and retrospective monitoring of time-series datasets. It provides two distinct, high-value detection paradigms: comprehensive series analysis via the POST /timeseries/entire/detect endpoint and real-time, point-in-time validation through POST /timeseries/last/detect. The first functionality ingests a complete historical dataset, constructs a tailored statistical or machine learning model to establish a baseline of normal behavior, and returns anomaly scores or labels for every point in the series, enabling retrospective batch analysis for quarterly reports or post-incident reviews. The second operates as a streaming endpoint, training a model exclusively on data preceding a final point and then evaluating that final point alone, which is ideal for live monitoring systems, alerting on the most recent data ingestion, or validating new data points before they corrupt a production database. Typical enterprise applications span across IT infrastructure monitoring (detecting CPU spikes or latency jumps), financial services (flagging fraudulent transaction patterns or irregular trading volumes), and industrial IoT (predicting equipment failure through sensor vibration or temperature anomalies).