elasticsearch

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Overview

The elasticsearch-mcp-server is a Model Context Protocol (MCP) server that enables AI assistants and agents to interact directly with Elasticsearch clusters through a structured, AI-friendly interface. It allows AI-driven workflows to search, index, analyze, and manage data stored in Elasticsearch without writing custom client code or switching to separate tooling.

This server is well suited for search-driven applications, log analysis, observability workflows, and any AI use case that depends on fast, flexible querying over large datasets.

Transport

streamable-http

Tools

  • esql
  • get_mappings
  • get_shards
  • list_indices
  • search

Key Capabilities

  • Powerful search and retrieval — Run full-text, filtered, and structured searches using Elasticsearch’s query engine.
  • Schema and mapping introspection — Inspect index mappings and settings programmatically.
  • Data ingestion and updates — Index, update, and delete documents as part of AI-driven workflows.
  • Text analysis support — Leverage Elasticsearch analyzers and tokenization for search diagnostics.
  • Observability and analytics workflows — Query logs, metrics, or event data conversationally.

How It Works

The elasticsearch-mcp-server runs as a local or containerized MCP service and connects to an Elasticsearch cluster using standard connection details (host, port, authentication credentials, and TLS settings). Once configured, it exposes Elasticsearch operations as MCP tools that AI clients can invoke.

When an AI assistant calls a tool, the server translates the MCP request into an Elasticsearch API call, executes it against the cluster, and returns structured results over the MCP protocol. Query responses, aggregations, and metadata are normalized so they can be easily reasoned over or summarized by the assistant.

By abstracting Elasticsearch’s REST APIs and query syntax behind MCP tools, the server allows AI agents to explore datasets, diagnose issues, and build search-driven workflows through natural language — enabling use cases like “find all error logs from the last hour,” “explain this index’s mapping,” or “index new documents and verify they’re searchable” without bespoke integration code.