Content
# 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.
## Features
- **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
## Available Tools
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
## Installation
### Prerequisites
- Python 3.10 or higher
- `uv` package manager (recommended for MCP servers)
### Setup
1. Install `uv` if you don't have it already:
```bash
# 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:
```bash
git clone https://github.com/JustTryAI/databricks-mcp-server.git
cd databricks-mcp-server
```
3. Set up the project with `uv`:
```bash
# 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:
```bash
# 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.
## Running the MCP Server
To start the MCP server, run:
```bash
# 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:
```bash
# Windows
.\scripts\start_mcp_server.ps1
# Linux/Mac
./scripts/start_mcp_server.sh
```
## Querying Databricks Resources
The repository includes utility scripts to quickly view Databricks resources:
```bash
# View all clusters
uv run scripts/show_clusters.py
# View all notebooks
uv run scripts/show_notebooks.py
```
## Project Structure
```
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.
## Development
### Code Standards
- 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
### Linting
The project uses the following linting tools:
```bash
# Run all linters
uv run pylint src/ tests/
uv run flake8 src/ tests/
uv run mypy src/
```
## Testing
The project uses pytest for testing. To run the tests:
```bash
# 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:
```bash
# 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.
## Documentation
- 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
## Examples
Check the `examples/` directory for usage examples. To run examples:
```bash
# Run example scripts with uv
uv run examples/direct_usage.py
uv run examples/mcp_client_usage.py
```
## Contributing
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
## License
This project is licensed under the MIT License - see the LICENSE file for details.
