databricks-mcp-server

A Model Completion Protocol (MCP) server for Databricks that provides access to Databricks functionality via the MCP protocol. This allows LLM-powered tools to interact with Databricks clusters, jobs, notebooks, and more.

• MCP Protocol Support: Implements the MCP protocol to allow LLMs to interact with Databricks
• Databricks API Integration: Provides access to Databricks REST API functionality
• Tool Registration: Exposes Databricks functionality as MCP tools
• Async Support: Built with asyncio for efficient operation

The Databricks MCP Server exposes the following tools:

• list_clusters: List all Databricks clusters
• create_cluster: Create a new Databricks cluster
• terminate_cluster: Terminate a Databricks cluster
• get_cluster: Get information about a specific Databricks cluster
• start_cluster: Start a terminated Databricks cluster
• list_jobs: List all Databricks jobs
• run_job: Run a Databricks job
• list_notebooks: List notebooks in a workspace directory
• export_notebook: Export a notebook from the workspace
• list_files: List files and directories in a DBFS path
• execute_sql: Execute a SQL statement

• Python 3.10 or higher
• uv package manager (recommended for MCP servers)

1. Install uv if you don't have it already:

   # MacOS/Linux
   curl -LsSf https://astral.sh/uv/install.sh | sh
   
   # Windows (in PowerShell)
   irm https://astral.sh/uv/install.ps1 | iex

   Restart your terminal after installation.

2. Clone the repository:

   git clone https://github.com/JustTryAI/databricks-mcp-server.git
   cd databricks-mcp-server

3. Set up the project with uv:

   # Create and activate virtual environment
   uv venv
   
   # On Windows
   .\.venv\Scripts\activate
   
   # On Linux/Mac
   source .venv/bin/activate
   
   # Install dependencies in development mode
   uv pip install -e .
   
   # Install development dependencies
   uv pip install -e ".[dev]"

4. Set up environment variables:

   # Windows
   set DATABRICKS_HOST=https://your-databricks-instance.azuredatabricks.net
   set DATABRICKS_TOKEN=your-personal-access-token
   
   # Linux/Mac
   export DATABRICKS_HOST=https://your-databricks-instance.azuredatabricks.net
   export DATABRICKS_TOKEN=your-personal-access-token

   You can also create an .env file based on the .env.example template.

To start the MCP server, run:

# Windows
.\start_mcp_server.ps1

# Linux/Mac
./start_mcp_server.sh

These wrapper scripts will execute the actual server scripts located in the scripts directory. The server will start and be ready to accept MCP protocol connections.

You can also directly run the server scripts from the scripts directory:

# Windows
.\scripts\start_mcp_server.ps1

# Linux/Mac
./scripts/start_mcp_server.sh

The repository includes utility scripts to quickly view Databricks resources:

# View all clusters
uv run scripts/show_clusters.py

# View all notebooks
uv run scripts/show_notebooks.py

databricks-mcp-server/
├── src/                             # Source code
│   ├── __init__.py                  # Makes src a package
│   ├── __main__.py                  # Main entry point for the package
│   ├── main.py                      # Entry point for the MCP server
│   ├── api/                         # Databricks API clients
│   ├── core/                        # Core functionality
│   ├── server/                      # Server implementation
│   │   ├── databricks_mcp_server.py # Main MCP server
│   │   └── app.py                   # FastAPI app for tests
│   └── cli/                         # Command-line interface
├── tests/                           # Test directory
├── scripts/                         # Helper scripts
│   ├── start_mcp_server.ps1         # Server startup script (Windows)
│   ├── run_tests.ps1                # Test runner script
│   ├── show_clusters.py             # Script to show clusters
│   └── show_notebooks.py            # Script to show notebooks
├── examples/                        # Example usage
├── docs/                            # Documentation
└── pyproject.toml                   # Project configuration

See project_structure.md for a more detailed view of the project structure.

• Python code follows PEP 8 style guide with a maximum line length of 100 characters
• Use 4 spaces for indentation (no tabs)
• Use double quotes for strings
• All classes, methods, and functions should have Google-style docstrings
• Type hints are required for all code except tests

The project uses the following linting tools:

# Run all linters
uv run pylint src/ tests/
uv run flake8 src/ tests/
uv run mypy src/

The project uses pytest for testing. To run the tests:

# Run all tests with our convenient script
.\scripts\run_tests.ps1

# Run with coverage report
.\scripts\run_tests.ps1 -Coverage

# Run specific tests with verbose output
.\scripts\run_tests.ps1 -Verbose -Coverage tests/test_clusters.py

You can also run the tests directly with pytest:

# Run all tests
uv run pytest tests/

# Run with coverage report
uv run pytest --cov=src tests/ --cov-report=term-missing

A minimum code coverage of 80% is the goal for the project.

• API documentation is generated using Sphinx and can be found in the docs/api directory
• All code includes Google-style docstrings
• See the examples/ directory for usage examples

Check the examples/ directory for usage examples. To run examples:

# Run example scripts with uv
uv run examples/direct_usage.py
uv run examples/mcp_client_usage.py

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

1. Ensure your code follows the project's coding standards
2. Add tests for any new functionality
3. Update documentation as necessary
4. Verify all tests pass before submitting

This project is licensed under the MIT License - see the LICENSE file for details.