Metabase MCP Server

Metabase MCP Server

By hyeongjun-dev GitHub

A Model Context Protocol server that integrates AI assistants with Metabase analytics platform

metabase mcp-server
Overview

What is Metabase MCP Server?

Metabase MCP Server is a Model Context Protocol server that integrates AI assistants with the Metabase analytics platform, allowing for seamless interaction with analytics data.

How to use Metabase MCP Server?

To use the Metabase MCP Server, configure it with your Metabase instance credentials and run it. You can interact with it through various tools exposed for AI assistants.

Key features of Metabase MCP Server?

  • Navigate Metabase resources via intuitive metabase:// URIs
  • Support for both session-based and API key authentication
  • JSON-formatted responses for easy consumption by AI assistants
  • Comprehensive logging for debugging and monitoring
  • Robust error handling with clear messages

Use cases of Metabase MCP Server?

  1. Integrating AI assistants with Metabase for data queries.
  2. Automating data retrieval and analysis through conversational interfaces.
  3. Enhancing analytics workflows with AI-driven insights.

FAQ from Metabase MCP Server?

  • What is the primary purpose of the Metabase MCP Server?

It serves as a bridge between AI assistants and the Metabase analytics platform, enabling direct interaction with analytics data.

  • What authentication methods are supported?

The server supports both session-based and API key authentication.

  • Is there a Docker image available?

Yes, a Docker image is available for containerized deployment.

Content

Metabase MCP Server

Author: Hyeongjun Yu (@hyeongjun-dev)

smithery badge

A Model Context Protocol server that integrates AI assistants with Metabase analytics platform.

Overview

This TypeScript-based MCP server provides seamless integration with the Metabase API, enabling AI assistants to directly interact with your analytics data. Designed for Claude and other MCP-compatible AI assistants, this server acts as a bridge between your analytics platform and conversational AI.

Key Features

  • Resource Access: Navigate Metabase resources via intuitive metabase:// URIs
  • Two Authentication Methods: Support for both session-based and API key authentication
  • Structured Data Access: JSON-formatted responses for easy consumption by AI assistants
  • Comprehensive Logging: Detailed logging for easy debugging and monitoring
  • Error Handling: Robust error handling with clear error messages

Available Tools

The server exposes the following tools for AI assistants:

  • list_dashboards: Retrieve all available dashboards in your Metabase instance
  • list_cards: Get all saved questions/cards in Metabase
  • list_databases: View all connected database sources
  • execute_card: Run saved questions and retrieve results with optional parameters
  • get_dashboard_cards: Extract all cards from a specific dashboard
  • execute_query: Execute custom SQL queries against any connected database

Configuration

The server supports two authentication methods:

Option 1: Username and Password Authentication

# Required
METABASE_URL=https://your-metabase-instance.com
METABASE_USER_EMAIL=your_email@example.com
METABASE_PASSWORD=your_password

# Optional
LOG_LEVEL=info # Options: debug, info, warn, error, fatal
# Required
METABASE_URL=https://your-metabase-instance.com
METABASE_API_KEY=your_api_key

# Optional
LOG_LEVEL=info # Options: debug, info, warn, error, fatal

You can set these environment variables directly or use a .env file with dotenv.

Installation

Prerequisites

  • Node.js 18.0.0 or higher
  • An active Metabase instance with appropriate credentials

Development Setup

# Install dependencies
npm install

# Build the project
npm run build

# Start the server
npm start

# For development with auto-rebuild
npm run watch

Claude Desktop Integration

To use with Claude Desktop, add this server configuration:

MacOS: Edit ~/Library/Application Support/Claude/claude_desktop_config.json

Windows: Edit %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "metabase-mcp-server": {
      "command": "/absolute/path/to/metabase-mcp-server/build/index.js",
      "env": {
        "METABASE_URL": "https://your-metabase-instance.com",
        "METABASE_USER_EMAIL": "your_email@example.com",
        "METABASE_PASSWORD": "your_password"
        // Or alternatively, use API key authentication
        // "METABASE_API_KEY": "your_api_key"
      }
    }
  }
}

Alternatively, you can use the Smithery hosted version via npx with JSON configuration:

API Key Authentication:

{
  "mcpServers": {
    "metabase-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli@latest",
        "run",
        "@hyeongjun-dev/metabase-mcp-server",
        "--config",
        "{\"metabaseUrl\":\"https://your-metabase-instance.com\",\"metabaseApiKey\":\"your_api_key\",\"metabasePassword\":\"\",\"metabaseUserEmail\":\"\"}"
      ]
    }
  }
}

Username and Password Authentication:

{
  "mcpServers": {
    "metabase-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli@latest",
        "run",
        "@hyeongjun-dev/metabase-mcp-server",
        "--config",
        "{\"metabaseUrl\":\"https://your-metabase-instance.com\",\"metabaseApiKey\":\"\",\"metabasePassword\":\"your_password\",\"metabaseUserEmail\":\"your_email@example.com\"}"
      ]
    }
  }
}

Debugging

Since MCP servers communicate over stdio, use the MCP Inspector for debugging:

npm run inspector

The Inspector will provide a browser-based interface for monitoring requests and responses.

Docker Support

A Docker image is available for containerized deployment:

# Build the Docker image
docker build -t metabase-mcp-server .

# Run the container with environment variables
docker run -e METABASE_URL=https://your-metabase.com \
           -e METABASE_API_KEY=your_api_key \
           metabase-mcp-server

Security Considerations

  • We recommend using API key authentication for production environments
  • Keep your API keys and credentials secure
  • Consider using Docker secrets or environment variables instead of hardcoding credentials
  • Apply appropriate network security measures to restrict access to your Metabase instance

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

No tools information available.

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