Elasticsearch MCP Server

This project implements an MCP (Model Context Protocol) server for Elasticsearch, providing tools and resources to interact with Elasticsearch clusters.

Features

Tools

• list_indices: Lists all indices in the Elasticsearch cluster

• get_mappings: Gets the mappings for a specific index

• search: Performs an Elasticsearch search with a provided query DSL

• search_with_query_string: Performs a search with a simple query string

• get_index_stats: Gets statistics for a specific index

Resources

• elasticsearch://indices: Lists all Elasticsearch indices

• elasticsearch://index/{index_name}: Gets detailed information about a specific index

• elasticsearch://mapping/{index_name}: Gets mapping information for a specific index

Related MCP server: Elasticsearch MCP Server

Prerequisites

• Python 3.7+

• Elasticsearch Python client

• MCP SDK

• Elasticsearch cluster credentials (Cloud ID and API Key)

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/elasticsearch-mcp-server.git
   cd elasticsearch-mcp-server

2. Install the required dependencies:

   pip install -r requirements.txt

3. Set up environment variables:

   • Copy the example environment file: cp .env.example .env

   • Edit the .env file and add your Elasticsearch credentials

   Or set them directly in your shell:

   export ES_CLOUD_ID=your_elasticsearch_cloud_id
   export ES_API_KEY=your_elasticsearch_api_key

Configuring the MCP Server for Claude

The configure_mcp_server.py script helps you set up the Elasticsearch MCP server in Claude's MCP settings file. This allows Claude to connect to your Elasticsearch cluster through the MCP server.

python configure_mcp_server.py your_cloud_id your_api_key

This script:

1. Takes your Elasticsearch Cloud ID and API Key as command-line arguments

2. Locates or creates the Claude MCP settings file

3. Adds or updates the Elasticsearch MCP server configuration

4. Sets the environment variables needed for the server to connect to your Elasticsearch cluster

After running this script, restart VS Code to apply the changes. Claude will then be able to use the Elasticsearch MCP server to interact with your Elasticsearch cluster.

Testing the MCP Resources

Option 1: Using the Test Script

We've provided a test script that starts the MCP server and provides instructions for testing:

# Make the script executable if needed
chmod +x test_es_mcp.sh

# Run the test script
ES_CLOUD_ID=your_cloud_id ES_API_KEY=your_api_key ./test_es_mcp.sh

The script will:

1. Start the MCP server in the background

2. Provide instructions for testing the resources

3. Keep the server running until you press Ctrl+C

Option 2: Manual Testing

1. Start the MCP server:

   ES_CLOUD_ID=your_cloud_id ES_API_KEY=your_api_key python es_mcp_server.py

2. In Claude, use the access_mcp_resource tool to access the resources:

   a. List all indices:

   <access_mcp_resource>
   <server_name>elasticsearch-mcp-server</server_name>
   <uri>elasticsearch://indices</uri>
   </access_mcp_resource>

   b. Get information about a specific index:

   <access_mcp_resource>
   <server_name>elasticsearch-mcp-server</server_name>
   <uri>elasticsearch://index/your_index_name</uri>
   </access_mcp_resource>

   c. Get mapping for a specific index:

   <access_mcp_resource>
   <server_name>elasticsearch-mcp-server</server_name>
   <uri>elasticsearch://mapping/your_index_name</uri>
   </access_mcp_resource>

Option 3: Using the Python Test Script

We've also provided a Python test script that demonstrates how to access the resources:

ES_CLOUD_ID=your_cloud_id ES_API_KEY=your_api_key python test_es_resources.py

Resource Details

elasticsearch://indices

Returns a JSON array of all indices in the Elasticsearch cluster, including:

• Index name

• Health status

• Status

• Document count

• Size

elasticsearch://index/{index_name}

Returns detailed information about a specific index, including:

• Index name

• Settings

• Statistics (document count, size in bytes and MB)

elasticsearch://mapping/{index_name}

Returns mapping information for a specific index, including:

• Complete mapping definition

• Field count

• Field type distribution

Error Handling

All resources include proper error handling and validation:

• If an index doesn't exist, the resource will return an appropriate error message

• If there's an issue connecting to Elasticsearch, the resource will return an error message

• All exceptions are caught and returned as readable error messages

Contributing

Contributions are welcome! Here's how you can contribute:

1. Fork the repository

2. Create a feature branch: git checkout -b feature/your-feature-name

3. Commit your changes: git commit -am 'Add some feature'

4. Push to the branch: git push origin feature/your-feature-name

5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.