Metabase MCP Server

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

Overview

This MCP server provides integration with the Metabase API, enabling LLM with MCP capabilites to directly interact with your analytics data, 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:

Data Access Tools

• 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
• list_collections: List all collections in Metabase
• list_tables: List all tables in a specific database
• get_table_fields: Get all fields/columns in a specific table

Execution Tools

• execute_card: Run saved questions and retrieve results with optional parameters
• execute_query: Execute custom SQL queries against any connected database

Dashboard Management

• get_dashboard_cards: Extract all cards from a specific dashboard
• create_dashboard: Create a new dashboard with specified name and parameters
• update_dashboard: Update an existing dashboard's name, description, or parameters
• delete_dashboard: Delete a dashboard
• add_card_to_dashboard: Add or update cards in a dashboard with position specifications and optional tab assignment

Card/Question Management

• create_card: Create a new question/card with SQL query
• update_card_visualization: Update visualization settings for a card

Collection Management

• create_collection: Create a new collection to organize dashboards and questions

Configuration

The server supports two authentication methods:

Option 1: Username and Password Authentication

# Required
METABASE_URL=https://your-metabase-instance.com
[email protected]
METABASE_PASSWORD=your_password

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

Option 2: API Key Authentication (Recommended for Production)

# 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.

Deployment with Smithery

To use this MCP server with Claude or other AI assistants, fork this repository and deploy using Smithery:

Steps to Deploy:

1. Fork this repository to your GitHub account
2. Go to Smithery and connect with your GitHub account
3. Deploy the forked repository through Smithery's interface

Claude Desktop Integration

Configure your Claude Desktop to use the Smithery-hosted version:

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

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

API Key Authentication:

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

Username and Password Authentication:

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

Security Considerations

• recommend using API key authentication for production environments
• Keep your API keys and credentials secure
• Consider using 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.