Metabase MCP Server

Integrations

  • Provides containerized deployment of the MCP server through Docker and Docker Compose, enabling easy setup and configuration.

  • Allows AI assistants to interact with Metabase databases and actions, including listing and exploring databases, retrieving metadata about schemas and tables, visualizing table relationships, running SQL queries, and executing Metabase actions with parameters.

Metabase MCP Server

A Model Control Protocol (MCP) server that enables AI assistants to interact with Metabase databases and actions.

![Metabase MCP Server]

Overview

The Metabase MCP Server provides a bridge between AI assistants and Metabase, allowing AI models to:

  • List and explore databases configured in Metabase
  • Retrieve detailed metadata about database schemas, tables, and fields
  • Visualize relationships between tables in a database
  • List and execute Metabase actions
  • Perform operations on Metabase data through a secure API

This server implements the [Model Control Protocol (MCP)] specification, making it compatible with AI assistants that support MCP tools.

Features

  • Database Exploration: List all databases and explore their schemas
  • Metadata Retrieval: Get detailed information about tables, fields, and relationships
  • Relationship Visualization: Generate visual representations of database relationships
  • Action Management: List, view details, and execute Metabase actions
  • Secure API Key Handling: Store API keys encrypted and prevent exposure
  • Web Interface: Test and debug functionality through a user-friendly web interface
  • Docker Support: Easy deployment with Docker and Docker Compose

Prerequisites

  • Metabase instance (v0.46.0 or higher recommended)
  • Metabase API key with appropriate permissions
  • Docker (for containerized deployment)
  • Python 3.10+ (for local development)

Installation

  1. Clone this repository:
    git clone https://github.com/yourusername/metabase-mcp.git cd metabase-mcp
  2. Build and run the Docker container:
    docker-compose up -d
  3. Access the configuration interface at http://localhost:5001

Manual Installation

  1. Clone this repository:
    git clone https://github.com/yourusername/metabase-mcp.git cd metabase-mcp
  2. Install dependencies:
    pip install -r requirements.txt
  3. Run the configuration interface:
    python -m src.server.web_interface
  4. Access the configuration interface at http://localhost:5000

Configuration

  1. Open the web interface in your browser
  2. Enter your Metabase URL (e.g., http://localhost:3000)
  3. Enter your Metabase API key
  4. Click "Save Configuration" and test the connection

Obtaining a Metabase API Key

  1. Log in to your Metabase instance as an administrator
  2. Go to Settings > Admin settings > API Keys
  3. Create a new API key with appropriate permissions
  4. Copy the generated key for use in the MCP server

Usage

Running the MCP Server

After configuration, you can run the MCP server:

# Using Docker docker run -p 5001:5000 metabase-mcp # Manually python -m src.server.mcp_server

Available Tools

The MCP server provides the following tools to AI assistants:

  1. list_databases: List all databases configured in Metabase
  2. get_database_metadata: Get detailed metadata for a specific database
  3. db_overview: Get a high-level overview of all tables in a database
  4. table_detail: Get detailed information about a specific table
  5. visualize_database_relationships: Generate a visual representation of database relationships
  6. run_database_query: Execute a SQL query against a database
  7. list_actions: List all actions configured in Metabase
  8. get_action_details: Get detailed information about a specific action
  9. execute_action: Execute a Metabase action with parameters

Testing Tools via Web Interface

The web interface provides a testing area for each tool:

  1. List Databases: View all databases configured in Metabase
  2. Get Database Metadata: View detailed schema information for a database
  3. DB Overview: View a concise list of all tables in a database
  4. Table Detail: View detailed information about a specific table
  5. Visualize Database Relationships: Generate a visual representation of table relationships
  6. Run Query: Execute SQL queries against databases
  7. List Actions: View all actions configured in Metabase
  8. Get Action Details: View detailed information about a specific action
  9. Execute Action: Test executing an action with parameters

Security Considerations

  • API keys are stored encrypted at rest
  • The web interface never displays API keys in plain text
  • All API requests use HTTPS when configured with a secure Metabase URL
  • The server should be deployed behind a secure proxy in production environments

Development

Project Structure

metabase-mcp/ ├── src/ │ ├── api/ # Metabase API client │ ├── config/ # Configuration management │ ├── server/ # MCP and web servers │ └── tools/ # Tool implementations ├── templates/ # Web interface templates ├── docker-compose.yml # Docker Compose configuration ├── Dockerfile # Docker build configuration ├── requirements.txt # Python dependencies └── README.md # Documentation

Adding New Tools

To add a new tool:

  1. Implement the tool function in src/tools/
  2. Register the tool in src/server/mcp_server.py
  3. Add a testing interface in templates/config.html (optional)
  4. Add a route in src/server/web_interface.py (if adding a testing interface)

Troubleshooting

Common Issues

  • Connection Failed: Ensure your Metabase URL is correct and accessible
  • Authentication Error: Verify your API key has the necessary permissions
  • Docker Network Issues: When using Docker, ensure proper network configuration

Logs

Check the logs for detailed error information:

# Docker logs docker logs metabase-mcp # Manual execution logs # Logs are printed to the console

Contributing

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

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security - not tested
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license - not found
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quality - not tested

A Model Control Protocol server that enables AI assistants to interact with Metabase databases, allowing models to explore database schemas, retrieve metadata, visualize relationships, and execute actions.

  1. Overview
    1. Features
      1. Prerequisites
        1. Installation
          1. Using Docker (Recommended)
          2. Manual Installation
        2. Configuration
          1. Obtaining a Metabase API Key
        3. Usage
          1. Running the MCP Server
          2. Available Tools
          3. Testing Tools via Web Interface
        4. Security Considerations
          1. Development
            1. Project Structure
            2. Adding New Tools
          2. Troubleshooting
            1. Common Issues
            2. Logs
          3. Contributing
            ID: 9xo1x48d7y