MCP Server: Elasticsearch semantic search tool

MCP Server: Elasticsearch semantic search tool

By jedrazb GitHub

MCP server to search up-to-date elasticsearch docs

Overview

what is MCP Server?

MCP Server is a Python implementation designed for semantic search through blog posts indexed in Elasticsearch, specifically tailored for Search Labs.

how to use MCP Server?

To use the MCP Server, set up your Elasticsearch environment by adding the ES_URL and ES_AP_KEY to a .env file, then start the server using the command make dev. Access the MCP Inspector at http://localhost:5173 to interact with the server.

key features of MCP Server?

  • Semantic search capabilities for indexed blog posts.
  • Integration with Claude Desktop for enhanced functionality.
  • Easy setup and configuration for Elasticsearch.

use cases of MCP Server?

  1. Performing semantic searches on blog content.
  2. Integrating with desktop applications for improved search experiences.
  3. Crawling and indexing blog posts for efficient retrieval.

FAQ from MCP Server?

  • What is required to run the MCP Server?

You need to have Elasticsearch set up and the blog posts indexed in the search-labs-posts index.

  • Can I integrate MCP Server with other applications?

Yes! MCP Server can be integrated with Claude Desktop and potentially other applications that support API interactions.

  • Is there a specific version of Elasticsearch required?

The MCP Server is designed to work with the latest versions of Elasticsearch, but ensure compatibility with the ELSER model for semantic search.

Content

MCP Server: Elasticsearch semantic search tool

Demo repo for: https://j.blaszyk.me/tech-blog/mcp-server-elasticsearch-semantic-search/

Table of Contents

Overview

This repository provides a Python implementation of an MCP server for semantic search through Search Labs blog posts indexed in Elasticsearch.

It assumes you've crawled the blog posts and stored them in the search-labs-posts index using Elastic Open Crawler.

Running the MCP Server

Add ES_URL and ES_AP_KEY into .env file, (take a look here for generating api key with minimum permissions)

Start the server in MCP Inspector:

make dev

Once running, access the MCP Inspector at: http://localhost:5173

Integrating with Claude Desktop

To add the MCP server to Claude Desktop:

make install-claude-config

This updates claude_desktop_config.json in your home directory. On the next restart, the Claude app will detect the server and load the declared tool.

Crawling Search Labs Blog Posts

1. Verify Crawler Setup

To check if the Elastic Open Crawler works, run:

docker run --rm \
  --entrypoint /bin/bash \
  -v "$(pwd)/crawler-config:/app/config" \
  --network host \
  docker.elastic.co/integrations/crawler:latest \
  -c "bin/crawler crawl config/test-crawler.yml"

This should print crawled content from a single page.

2. Configure Elasticsearch

Set up Elasticsearch URL and API Key.

Generate an API key with minimum crawler permissions:

POST /_security/api_key
{
  "name": "crawler-search-labs",
  "role_descriptors": {
    "crawler-search-labs-role": {
      "cluster": ["monitor"],
      "indices": [
        {
          "names": ["search-labs-posts"],
          "privileges": ["all"]
        }
      ]
    }
  },
  "metadata": {
    "application": "crawler"
  }
}

Copy the encoded value from the response and set it as API_KEY.

Ensure the search-labs-posts index exists. If not, create it:

PUT search-labs-posts

Update the mapping to enable semantic search:

PUT search-labs-posts/_mappings
{
  "properties": {
    "body": {
      "type": "text",
      "copy_to": "semantic_body"
    },
    "semantic_body": {
      "type": "semantic_text",
      "inference_id": ".elser-2-elasticsearch"
    }
  }
}

The body field is indexed as semantic text using Elasticsearch’s ELSER model.

4. Start Crawling

Run the crawler to populate the index:

docker run --rm \
  --entrypoint /bin/bash \
  -v "$(pwd)/crawler-config:/app/config" \
  --network host \
  docker.elastic.co/integrations/crawler:latest \
  -c "bin/crawler crawl config/elastic-search-labs-crawler.yml"

TIP

If using a fresh Elasticsearch cluster, wait for the ELSER model to start before indexing.

5. Verify Indexed Documents

Check if the documents were indexed:

GET search-labs-posts/_count

This will return the total document count in the index. You can also verify in Kibana.

Done! You can now perform semantic searches on Search Labs blog posts

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