
elastic semantic search mcp server
MCP server to search up-to-date elasticsearch docs
Repository Info
About This Server
MCP server to search up-to-date elasticsearch docs
Model Context Protocol (MCP) - This server can be integrated with AI applications to provide additional context and capabilities, enabling enhanced AI interactions and functionality.
Documentation
MCP Server: Elasticsearch semantic search tool
Demo repo for: https://j.blaszyk.me/tech-blog/mcp-server-elasticsearch-semantic-search/
Table of Contents
- Overview
- Running the MCP Server
- Integrating with Claude Desktop
- Crawling Search Labs Blog Posts
-
- Verify Crawler Setup
-
- Configure Elasticsearch
-
- Update Index Mapping for Semantic Search
-
- Start Crawling
-
- Verify Indexed Documents
-
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 "$(pwd)/crawler-config:/app/config" \
--network host \
docker.elastic.co/integrations/crawler:latest \
-c "bin/crawler crawl config/test-crawler.yml"
This should print crawled content from a single page.
2. Configure Elasticsearch
Set up Elasticsearch URL and API Key.
Generate an API key with minimum crawler permissions:
POST /_security/api_key
{
"name": "crawler-search-labs",
"role_descriptors": {
"crawler-search-labs-role": {
"cluster": ["monitor"],
"indices": [
{
"names": ["search-labs-posts"],
"privileges": ["all"]
}
]
}
},
"metadata": {
"application": "crawler"
}
}
Copy the encoded value from the response and set it as API_KEY.
3. Update Index Mapping for Semantic Search
Ensure the search-labs-posts index exists. If not, create it:
PUT search-labs-posts
Update the mapping to enable semantic search:
PUT search-labs-posts/_mappings
{
"properties": {
"body": {
"type": "text",
"copy_to": "semantic_body"
},
"semantic_body": {
"type": "semantic_text",
"inference_id": ".elser-2-elasticsearch"
}
}
}
The body field is indexed as semantic text using Elasticsearch’s ELSER model.
4. Start Crawling
Run the crawler to populate the index:
docker run --rm \
--entrypoint /bin/bash \
-v "$(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
Quick Start
Clone the repository
git clone https://github.com/jedrazb/elastic-semantic-search-mcp-serverInstall dependencies
cd elastic-semantic-search-mcp-server
npm installFollow the documentation
Check the repository's README.md file for specific installation and usage instructions.
Repository Details
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