Cube.js

A Model Context Protocol (MCP) server implementation for Cube.js, enabling seamless integration between AI assistants and Cube.js analytics platforms.

Overview

This project provides a FastMCP-based server that exposes Cube.js analytics capabilities through the Model Context Protocol. It allows AI models and applications to:

• List available data cubes and their metadata
• Query data using natural language-friendly interfaces
• Access measures, dimensions, and segments from your Cube.js instance
• Execute complex analytics queries programmatically

Features

• Cube Listing: Retrieve all available cubes with their measures, dimensions, and segments
• Query Support: Execute queries against Cube.js with flexible filtering and aggregation
• Metadata Access: Get detailed information about cube structure and relationships
• Async Support: Built on FastMCP for high-performance async operations
• Error Handling: Robust error handling with meaningful error messages
• Token Authentication: Secure API access with token-based authentication

Prerequisites

• Python 3.8 or higher
• Cube.js instance running and accessible
• pip package manager

Installation

1. Clone the repository:

git clone https://github.com/zsembek/Cube.js-MCP-server.git
cd Cube.js-MCP-server

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables:

cp .env.example .env

Edit .env with your Cube.js configuration:

CUBEJS_API_BASE_URL=http://localhost:4000/cubejs-api/v1
CUBEJS_API_TOKEN=your_api_token_here

Configuration

Environment Variables

• CUBEJS_API_BASE_URL: The base URL of your Cube.js API (default: http://localhost:4000/cubejs-api/v1)
• CUBEJS_API_TOKEN: Authentication token for Cube.js API (required if your instance requires authentication)

Claude Configuration

To use this MCP server with Claude or other compatible clients, add it to your configuration file (~/.config/Claude/claude_desktop_config.json):

{
    "mcpServers": {
        "cubejs": {
            "command": "uvx",
            "args": [
                "--with",
                "cubejs-mcp-server @ git+https://github.com/zsembek/Cube.js-MCP-server.git",
                "python",
                "-m",
                "cubejs_mcp.server"
            ],
            "env": {
                "CUBEJS_API_BASE_URL": "http://localhost:4000/cubejs-api/v1",
                "CUBEJS_API_TOKEN": "your_api_token"
            }
        }
    }
}

Usage

Running the Server

python server.py

The server will start and be ready to accept MCP protocol requests.

Available Tools

1. list_cubes()

Retrieves the list of available cubes with their metadata.

Returns: A dictionary containing:

• Cube names and descriptions
• Available measures for each cube
• Available dimensions for each cube
• Available segments for each cube

Example:

cubes = await list_cubes()

2. query_cube(cube_name, measures, dimensions, filters)

Execute a query against a specific cube.

Parameters:

• cube_name (string): Name of the cube to query
• measures (list): List of measures to include in the query
• dimensions (list): List of dimensions to group by
• filters (optional, list): List of filter conditions

Returns: Query results with aggregated data

Example:

result = await query_cube(
    cube_name="Orders",
    measures=["Orders.count", "Orders.total"],
    dimensions=["Orders.status"],
    filters=["Orders.created_date > 2024-01-01"]
)

Project Structure

.
├── cubejs_mcp/
│   ├── __init__.py        # Package initialization
│   └── server.py          # MCP server implementation
├── server.py              # Legacy entry point (kept for compatibility)
├── config.json            # Configuration file for MCP clients
├── pyproject.toml         # Python package configuration
├── requirements.txt       # Python dependencies
├── .env.example          # Environment variables template
└── README.md             # This file

Dependencies

• fastmcp: FastMCP framework for building MCP servers
• httpx: Async HTTP client for making requests to Cube.js
• python-dotenv: Environment variable management

See requirements.txt for specific versions.

Error Handling

The server includes comprehensive error handling for:

• Network connectivity issues
• Authentication failures
• Invalid cube or metric names
• API rate limiting
• Malformed queries

Error responses include descriptive messages to help diagnose issues.

Security Considerations

• Always keep your CUBEJS_API_TOKEN secret and never commit it to version control
• Use .env files with proper permissions (600 or restricted access)
• Consider using environment variables managed by your deployment platform
• Ensure your Cube.js instance is properly secured behind authentication/firewall

Development

Setting up Development Environment

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env

# Edit .env with your local Cube.js instance details
nano .env

Running Tests

Tests can be added to verify functionality. Use pytest or unittest frameworks.

Troubleshooting

Connection Issues

• Verify CUBEJS_API_BASE_URL is correct and Cube.js is running
• Check network connectivity to the Cube.js instance
• Ensure firewall allows connections

Authentication Errors

• Confirm CUBEJS_API_TOKEN is correct
• Check if your Cube.js instance requires authentication
• Verify token hasn't expired

Query Errors

• Ensure cube names, measures, and dimensions are spelled correctly
• Check if filters are properly formatted
• Verify you have permission to access the requested cubes

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

License

This project is open source and available under the MIT License.

Support

For issues, questions, or suggestions, please open an issue on the GitHub repository.

Resources

• Cube.js Documentation
• Model Context Protocol
• FastMCP Documentation