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Elasticsearch 7.x MCP Server

by imlewc
GitHub
Python
Apache 2.0
1
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Integrations

  • Utilizes environment variables through .env files to configure connection details for Elasticsearch, including host address, authentication credentials, and server port settings.

  • Enables interaction with Elasticsearch 7.x instances, supporting basic operations like ping and info, as well as complete search functionality including aggregation queries, highlighting, sorting, and other advanced search features.

  • Supports connection to Kibana as part of an Elasticsearch deployment through the Docker Compose setup, providing visualization and management capabilities for Elasticsearch data.

Elasticsearch 7.x MCP Server

An MCP server for Elasticsearch 7.x, providing compatibility with Elasticsearch 7.x versions.

Features

  • Provides an MCP protocol interface for interacting with Elasticsearch 7.x
  • Supports basic Elasticsearch operations (ping, info, etc.)
  • Supports complete search functionality, including aggregation queries, highlighting, sorting, and other advanced features
  • Easily access Elasticsearch functionality through any MCP client

Requirements

  • Python 3.10+
  • Elasticsearch 7.x (7.17.x recommended)

Installation

Installing via Smithery

To install Elasticsearch 7.x MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @imlewc/elasticsearch7-mcp-server --client claude

Manual Installation

pip install -e .

Environment Variables

The server requires the following environment variables:

  • ELASTIC_HOST: Elasticsearch host address (e.g., http://localhost:9200)
  • ELASTIC_USERNAME: Elasticsearch username
  • ELASTIC_PASSWORD: Elasticsearch password
  • MCP_PORT: (Optional) MCP server listening port, default 9999

Using Docker Compose

  1. Create a .env file and set ELASTIC_PASSWORD:
ELASTIC_PASSWORD=your_secure_password
  1. Start the services:
docker-compose up -d

This will start a three-node Elasticsearch 7.17.10 cluster, Kibana, and the MCP server.

Using an MCP Client

You can use any MCP client to connect to the MCP server:

from mcp import MCPClient client = MCPClient("localhost:9999") response = client.call("es-ping") print(response) # {"success": true}

API Documentation

Currently supported MCP methods:

  • es-ping: Check Elasticsearch connection
  • es-info: Get Elasticsearch cluster information
  • es-search: Search documents in Elasticsearch index

Search API Examples

# Basic search search_response = client.call("es-search", { "index": "my_index", "query": { "match": { "title": "search keywords" } }, "size": 10, "from": 0 })
Aggregation Query
# Aggregation query agg_response = client.call("es-search", { "index": "my_index", "size": 0, # Only need aggregation results, no documents "aggs": { "categories": { "terms": { "field": "category.keyword", "size": 10 } }, "avg_price": { "avg": { "field": "price" } } } })
# Advanced search with highlighting, sorting, and filtering advanced_response = client.call("es-search", { "index": "my_index", "query": { "bool": { "must": [ {"match": {"content": "search term"}} ], "filter": [ {"range": {"price": {"gte": 100, "lte": 200}}} ] } }, "sort": [ {"date": {"order": "desc"}}, "_score" ], "highlight": { "fields": { "content": {} } }, "_source": ["title", "date", "price"] })

Development

  1. Clone the repository
  2. Install development dependencies
  3. Run the server: elasticsearch7-mcp-server

License

[License in LICENSE file]

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security – no known vulnerabilities
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license - permissive license
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quality - confirmed to work

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Tools

Provides an MCP protocol interface for interacting with Elasticsearch 7.x databases, supporting comprehensive search functionality including aggregations, highlighting, and sorting.

  1. Features
    1. Requirements
      1. Installation
        1. Installing via Smithery
        2. Manual Installation
      2. Environment Variables
        1. Using Docker Compose
          1. Using an MCP Client
            1. API Documentation
              1. Search API Examples
            2. Development
              1. License

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