MCP Elasticsearch Server

MCP Elasticsearch Server
An MCP server that turns an Elasticsearch cluster into a first‑class AI tool, enabling structured search and metadata retrieval through a standardized protocol.

The MCP Elasticsearch server solves the friction that developers face when trying to expose search functionality from a production Elasticsearch cluster to AI assistants. Rather than writing custom connectors for each assistant or building REST wrappers, the server presents a uniform set of tools that any MCP‑compatible client can invoke. This abstraction removes boilerplate authentication handling, query construction, and response parsing, allowing developers to focus on higher‑level logic such as intent detection or conversational context.

At its core, the server offers three powerful tools: , , and . The first tool lists all indices, optionally filtered by a glob pattern, and returns health status, document counts, and storage size—useful for monitoring or data‑driven prompts. The second retrieves the field mappings of one or more indices, giving AI assistants insight into schema and data types without manual inspection. The third is a full‑DSL search executor that accepts any Elasticsearch query, supports sorting and aggregations, limits result size, and tracks execution time. All responses are JSON‑formatted with metadata, making them easy to consume by downstream pipelines or conversational agents.

Developers can integrate the server into their AI workflows in several ways. In Claude Desktop, a simple configuration block launches the MCP command with environment variables that supply the cluster URL and authentication credentials. In ApMentor, a similar configuration exposes the server as an , allowing scripts or notebooks to call its tools via standard MCP messages. Because the server supports context cancellation, long‑running queries can be aborted if the conversation moves on, preserving resources and keeping response times predictable.

Real‑world scenarios where this server shines include:

• Log analytics – A chatbot that can answer questions about log volumes or error rates by querying the appropriate indices.
• Product search – An AI assistant that performs complex, faceted searches on an e‑commerce catalog stored in Elasticsearch.
• Data discovery – A data scientist’s helper that lists available indices and their schemas before writing a query.
• Operational monitoring – Automated alerts that trigger when index health drops or document counts exceed thresholds.

Unique advantages of the MCP Elasticsearch server are its authentication flexibility (API key or basic auth), performance monitoring (execution time tracking built into responses), and context awareness (support for cancellation). These features give developers confidence that the server will work securely, efficiently, and reliably across diverse AI applications.