Elasticsearch MCP Server

Elasticsearch MCP Server

Connects to Elasticsearch databases using the Model Context Protocol, allowing users to query and interact with their Elasticsearch indices through natural language conversations.

Category
Visit Server

README

Elasticsearch MCP Server

This repository contains experimental features intended for research and evaluation and are not production-ready.

Connect to your Elasticsearch data directly from any MCP Client (like Claude Desktop) using the Model Context Protocol (MCP).

This server connects agents to your Elasticsearch data using the Model Context Protocol. It allows you to interact with your Elasticsearch indices through natural language conversations.

<a href="https://glama.ai/mcp/servers/@elastic/mcp-server-elasticsearch"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@elastic/mcp-server-elasticsearch/badge" alt="Elasticsearch Server MCP server" /> </a>

Available Tools

  • list_indices: List all available Elasticsearch indices
  • get_mappings: Get field mappings for a specific Elasticsearch index
  • search: Perform an Elasticsearch search with the provided query DSL
  • get_shards: Get shard information for all or specific indices

Prerequisites

  • An Elasticsearch instance
  • Elasticsearch authentication credentials (API key or username/password)
  • MCP Client (e.g. Claude Desktop)

Demo

https://github.com/user-attachments/assets/5dd292e1-a728-4ca7-8f01-1380d1bebe0c

Installation & Setup

Using the Published NPM Package

[!TIP] The easiest way to use Elasticsearch MCP Server is through the published npm package.

  1. Configure MCP Client

    • Open your MCP Client. See the list of MCP Clients, here we are configuring Claude Desktop.
    • Go to Settings > Developer > MCP Servers
    • Click Edit Config and add a new MCP Server with the following configuration:
    {
      "mcpServers": {
        "elasticsearch-mcp-server": {
          "command": "npx",
          "args": [
            "-y",
            "@elastic/mcp-server-elasticsearch"
          ],
          "env": {
            "ES_URL": "your-elasticsearch-url",
            "ES_API_KEY": "your-api-key"
          }
        }
      }
    }
    
  2. Start a Conversation

    • Open a new conversation in your MCP Client
    • The MCP server should connect automatically
    • You can now ask questions about your Elasticsearch data

Configuration Options

The Elasticsearch MCP Server supports configuration options to connect to your Elasticsearch:

[!NOTE] You must provide either an API key or both username and password for authentication.

Environment Variable Description Required
ES_URL Your Elasticsearch instance URL Yes
ES_API_KEY Elasticsearch API key for authentication No
ES_USERNAME Elasticsearch username for basic authentication No
ES_PASSWORD Elasticsearch password for basic authentication No
ES_CA_CERT Path to custom CA certificate for Elasticsearch SSL/TLS No

Developing Locally

[!NOTE] If you want to modify or extend the MCP Server, follow these local development steps.

  1. Use the correct Node.js version

    nvm use
    
  2. Install Dependencies

    npm install
    
  3. Build the Project

    npm run build
    
  4. Run locally in Claude Desktop App

    • Open Claude Desktop App
    • Go to Settings > Developer > MCP Servers
    • Click Edit Config and add a new MCP Server with the following configuration:
    {
      "mcpServers": {
        "elasticsearch-mcp-server-local": {
          "command": "node",
          "args": [
            "/path/to/your/project/dist/index.js"
          ],
          "env": {
            "ES_URL": "your-elasticsearch-url",
            "ES_API_KEY": "your-api-key"
          }
        }
      }
    }
    
  5. Debugging with MCP Inspector

    ES_URL=your-elasticsearch-url ES_API_KEY=your-api-key npm run inspector
    

    This will start the MCP Inspector, allowing you to debug and analyze requests. You should see:

    Starting MCP inspector...
    Proxy server listening on port 3000
    
    🔍 MCP Inspector is up and running at http://localhost:5173 🚀
    

Contributing

We welcome contributions from the community! For details on how to contribute, please see Contributing Guidelines.

Example Questions

[!TIP] Here are some natural language queries you can try with your MCP Client.

  • "What indices do I have in my Elasticsearch cluster?"
  • "Show me the field mappings for the 'products' index."
  • "Find all orders over $500 from last month."
  • "Which products received the most 5-star reviews?"

How It Works

  1. The MCP Client analyzes your request and determines which Elasticsearch operations are needed.
  2. The MCP server carries out these operations (listing indices, fetching mappings, performing searches).
  3. The MCP Client processes the results and presents them in a user-friendly format.

Security Best Practices

[!WARNING] Avoid using cluster-admin privileges. Create dedicated API keys with limited scope and apply fine-grained access control at the index level to prevent unauthorized data access.

You can create a dedicated Elasticsearch API key with minimal permissions to control access to your data:

POST /_security/api_key
{
  "name": "es-mcp-server-access",
  "role_descriptors": {
    "mcp_server_role": {
      "cluster": [
        "monitor"
      ],
      "indices": [
        {
          "names": [
            "index-1",
            "index-2",
            "index-pattern-*"
          ],
          "privileges": [
            "read",
            "view_index_metadata"
          ]
        }
      ]
    }
  }
}

License

This project is licensed under the Apache License 2.0.

Troubleshooting

  • Ensure your MCP configuration is correct.
  • Verify that your Elasticsearch URL is accessible from your machine.
  • Check that your authentication credentials (API key or username/password) have the necessary permissions.
  • If using SSL/TLS with a custom CA, verify that the certificate path is correct and the file is readable.
  • Look at the terminal output for error messages.

If you encounter issues, feel free to open an issue on the GitHub repository.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured