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MCP Server: Elasticsearch semantic search tool

@jedrazb

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Basic information

Category

Memory & Knowledge

Transports

stdio

Author

jedrazb

About MCP Server: Elasticsearch semantic search tool

MCP server to search up-to-date elasticsearch docs

MCP Server: Elasticsearch semantic search tool config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "elastic-semantic-search-mcp-server": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "\\"
      ]
    }
  }
}

MCP Server: Elasticsearch semantic search tool tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is MCP Server: Elasticsearch semantic search tool?

This Python MCP server enables semantic search on Search Labs blog posts indexed in Elasticsearch. It uses the ELSER model for semantic text retrieval and integrates with Claude Desktop via MCP protocol. Designed for developers building AI-powered search applications with Elasticsearch.

How to use MCP Server: Elasticsearch semantic search tool?

Set ES_URL and ES_AP_KEY in a .env file. Start the server with make dev and access the MCP Inspector at http://localhost:5173. To add to Claude Desktop, run make install-claude-config, which updates claude_desktop_config.json.

Key features of MCP Server: Elasticsearch semantic search tool

  • Semantic search over Elasticsearch documents using ELSER model
  • Crawls Search Labs blog posts with Elastic Open Crawler
  • MCP Inspector interface for testing and debugging
  • One‑command integration with Claude Desktop
  • Configuration via environment variables (URL and API key)

Use cases of MCP Server: Elasticsearch semantic search tool

  • Search indexed blog posts by meaning, not just keywords
  • Let Claude Desktop retrieve semantic answers from your Elasticsearch corpus
  • Prototype semantic search workflows with an MCP‑compatible AI assistant
  • Combine crawling and indexing for a complete search‑pipeline demo

Frequently asked questions

1

What dependencies does the server require?

You need a running Elasticsearch cluster (with the ELSER model available), the Elastic Open Crawler (Docker image), and an API key with crawl‑and‑search privileges.

2

Where does the data live?

All documents are stored in an Elasticsearch index named `search-labs-posts`. The crawler populates it; the MCP server queries it.

3

What transport or authentication does the server use?

The server connects to Elasticsearch via its REST API using the URL and API key supplied in the `.env` file. No additional MCP transport is configured beyond the standard MCP protocol.

4

What are the known limits?

The crawler and ELSER model must be fully set up before indexing—if using a fresh cluster, wait for the ELSER model to start. The server only works with the `search-labs-posts` index and assumes the mapping defined in the README.

5

How is this different from standard Elasticsearch search?

It uses `semantic_text` fields with the ELSER model, enabling term‑agnostic semantic matching rather than keyword‑based BM25 scoring.

Comments

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