What is Mcp Metabase

MCP_MetaBase is a Model Control Protocol (MCP) server that facilitates interaction between AI assistants and Metabase databases, enabling AI models to access and manipulate database information and actions.

Use cases

Use cases for MCP_MetaBase include enabling AI assistants to generate reports from Metabase data, visualizing database schemas for better understanding, and automating data retrieval and action execution in business applications.

How to use

To use MCP_MetaBase, you can either deploy it using Docker or manually install it. After installation, configure it with your Metabase API key and access the web interface to explore databases, retrieve metadata, and manage actions.

Key features

Key features of MCP_MetaBase include database exploration, metadata retrieval, relationship visualization, action management, secure API key handling, a user-friendly web interface, and Docker support for easy deployment.

Where to use

MCP_MetaBase can be used in data analytics, business intelligence, and AI development environments where interaction with Metabase databases is required.

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

Using Docker (Recommended)

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.