Opensearch Mcp Server Py

Analyzes data distribution patterns and field value frequencies within OpenSearch indices. Supports both single dataset analysis for understanding data characteristics and comparative analysis between two time periods to identify distribution changes. Automatically detects useful fields, calculates value distributions, groups numeric data, and computes divergence metrics. Useful for anomaly detection, data quality assessment, and trend analysis. We can use this tool to analyze the distribution of failures over time

Intelligent log pattern analysis tool for troubleshooting and anomaly detection in application logs. Use this tool when you need to: analyze error patterns in logs, identify unusual log sequences, compare log patterns between time periods, find root causes of system issues, detect anomalous behavior in application traces, or investigate performance problems. The tool automatically extracts meaningful patterns from raw log messages, groups similar patterns, identifies outliers, and provides insights for debugging. Essential for log-based troubleshooting, incident analysis, and proactive monitoring of system health.

Lists indices in the OpenSearch cluster. If an index name or pattern is specified, return only information about the provided index or index pattern. The include_detail flag controls output: if False, returns only index name(s); if True (default), returns full metadata.

Retrieves index mapping and setting information for an index in OpenSearch

Searches an index using a query written in query domain-specific language (DSL) in OpenSearch. PREREQUISITE: You need to know the mappings of the index before constructing queries.