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Metabase

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连接AI助手与Metabase分析平台的MCP服务器

Metabase MCP Server - Connect AI Assistants to Your Metabase Analytics

PyPI version Python 3.12+ License: MIT FastMCP

A high-performance Model Context Protocol (MCP) server for Metabase, enabling AI assistants like Claude, Cursor, and other MCP clients to interact seamlessly with your Metabase instance. Query databases, execute SQL, manage dashboards, and automate analytics workflows with natural language through AI-powered database operations.

Perfect for: Data analysts, developers, and teams looking to integrate AI assistants with their Metabase business intelligence platform for automated SQL queries, dashboard management, and data exploration.

Key Features

Database Operations

  • List Databases: Browse all configured Metabase databases
  • Table Discovery: Explore tables with metadata and descriptions
  • Field Inspection: Get detailed field/column information with smart pagination

Query & Analytics

  • SQL Execution: Run native SQL queries with parameter support and templating
  • Card Management: Execute, create, and manage Metabase questions/cards
  • Collection Organization: Create and manage collections for better organization
  • Natural Language Queries: Let AI assistants translate questions into SQL

Authentication & Security

  • API Key Support: Secure authentication via Metabase API keys (recommended)
  • Session-based Auth: Alternative email/password authentication
  • Environment Variables: Secure credential management via .env files

AI Assistant Integration

  • Claude Desktop: Native integration with Anthropic's Claude AI
  • Cursor IDE: Seamless integration for AI-assisted development
  • Any MCP Client: Compatible with all Model Context Protocol clients

Enhanced Performance & Reliability

  • Context-aware Logging: Real-time logging with debug, info, warning, and error levels visible to AI clients
  • Proper Error Handling: FastMCP ToolError exceptions for better error messages and debugging
  • Middleware Stack: Built-in error handling and logging middleware for production reliability
  • Best Practices: Follows latest FastMCP patterns with duplicate prevention and clean configuration
  • Modern Python: Uses Python 3.12+ type hints (| syntax) for better type safety

Quick Start

Prerequisites

  • Python 3.12+
  • Metabase instance with API access
  • uvx or uv package manager

Installation

Option 1: Using uvx (Easiest - No Installation Required)

# Run directly without installing (like npx for Python)
uvx metabase-mcp

# With environment variables
METABASE_URL=https://your-instance.com METABASE_API_KEY=your-key uvx metabase-mcp

Option 2: Install from PyPI

# Install globally
uv tool install metabase-mcp

# Or with pip
pip install metabase-mcp

# Then run
metabase-mcp

Option 3: Development Setup (From Source)

# Clone the repository
git clone https://github.com/cheukyin175/metabase-mcp.git
cd metabase-mcp

# Install dependencies
uv sync

# Run the server
uv run python server.py

Configuration

Create a .env file with your Metabase credentials:

cp .env.example .env

Configuration Options

Option 1: API Key Authentication (Recommended)

METABASE_URL=https://your-metabase-instance.com
METABASE_API_KEY=your-api-key-here

Option 2: Email/Password Authentication

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

Optional: Custom Host/Port for SSE/HTTP

HOST=localhost  # Default: 0.0.0.0
PORT=9000      # Default: 8000

Usage

Run the Server

Quick Start (No Setup Required)

# Run directly with uvx
uvx metabase-mcp

# With custom Metabase instance
METABASE_URL=https://your-instance.com METABASE_API_KEY=your-key uvx metabase-mcp

From Source (Development)

# STDIO transport (default)
uv run python server.py

# SSE transport (uses HOST=0.0.0.0, PORT=8000 by default)
uv run python server.py --sse

# HTTP transport (uses HOST=0.0.0.0, PORT=8000 by default)
uv run python server.py --http

# Custom host and port via environment variables
HOST=localhost PORT=9000 uv run python server.py --sse
HOST=192.168.1.100 PORT=8080 uv run python server.py --http

Cursor Integration

You can manually configure Cursor by editing your Cursor settings.

For SSE transport: You must start the server before using Cursor:

uv run python server.py --sse

Claude Desktop Integration

Option 1: Using uvx (Recommended)

Add this to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
    "mcpServers": {
        "metabase-mcp": {
            "command": "uvx",
            "args": ["metabase-mcp"],
            "env": {
                "METABASE_URL": "https://your-metabase-instance.com",
                "METABASE_API_KEY": "your-api-key-here"
            }
        }
    }
}

Option 2: Using Local Installation

If you've cloned the repository:

{
    "mcpServers": {
        "metabase-mcp": {
            "command": "uv",
            "args": [
                "run",
                "--directory",
                "/absolute/path/to/metabase-mcp",
                "python",
                "server.py"
            ],
            "env": {
                "METABASE_URL": "https://your-metabase-instance.com",
                "METABASE_API_KEY": "your-api-key-here"
            }
        }
    }
}

Option 3: Using FastMCP CLI

fastmcp install server.py -n "Metabase MCP"

Available Tools

Database Operations

ToolDescription
list_databasesList all configured databases in Metabase
list_tablesGet all tables in a specific database with metadata
get_table_fieldsRetrieve field/column information for a table

Query Operations

ToolDescription
execute_queryExecute native SQL queries with parameter support
execute_cardRun saved Metabase questions/cards

Card Management

ToolDescription
list_cardsList all saved questions/cards
create_cardCreate new questions/cards with SQL queries

Collection Management

ToolDescription
list_collectionsBrowse all collections
create_collectionCreate new collections for organization

Transport Methods

The server supports multiple transport methods:

  • STDIO (default): For IDE integration (Cursor, Claude Desktop)
  • SSE: Server-Sent Events for web applications
  • HTTP: Standard HTTP for API access
uv run python server.py                        # STDIO (default)
uv run python server.py --sse                  # SSE (HOST=0.0.0.0, PORT=8000)
uv run python server.py --http                 # HTTP (HOST=0.0.0.0, PORT=8000)
HOST=localhost PORT=9000 uv run python server.py --sse   # Custom host/port

Development

Setup Development Environment

# Install with dev dependencies
uv sync --group dev

# Or with pip
pip install -r requirements-dev.txt

Code Quality

# Run linting
uv run ruff check .

# Format code
uv run ruff format .

# Type checking
uv run mypy server.py

Usage Examples

Query Examples

# List all databases
databases = await list_databases()

# Execute a SQL query
result = await execute_query(
    database_id=1,
    query="SELECT * FROM users LIMIT 10"
)

# Create and run a card
card = await create_card(
    name="Active Users Report",
    database_id=1,
    query="SELECT COUNT(*) FROM users WHERE active = true",
    collection_id=2
)

Project Structure

metabase-mcp/
├── server.py                 # Main MCP server implementation
├── pyproject.toml           # Project configuration and dependencies
└── .env.example             # Environment variables template

Contributing

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

License

MIT License - see LICENSE file for details

Resources

Keywords & Topics

metabase mcp model-context-protocol claude cursor ai-assistant fastmcp sql database analytics business-intelligence bi data-analysis anthropic llm python automation api data-science query-builder natural-language-sql

Star History

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Use Cases

  • Natural Language Database Queries: Ask Claude to query your Metabase databases using plain English
  • Automated Report Generation: Use AI to create and manage Metabase cards and collections
  • Data Exploration: Let AI assistants help you discover insights from your data
  • SQL Query Assistance: Get help writing and optimizing SQL queries through AI
  • Dashboard Management: Automate the creation and organization of Metabase dashboards
  • Data Analysis Workflows: Integrate AI-powered analytics into your development workflow

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