Markov Databricks MCP

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.

This project is maintained by Olivier Debeuf De Rijcker [email protected].

Credit for the initial version goes to @JustTryAI.

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

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.

Clone the repository:

git clone https://github.com/markov-kernel/databricks-mcp.git
cd databricks-mcp

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]"

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.

Running the MCP Server

Standalone

To start the MCP server directly for testing or development, run:

# Activate your virtual environment if not already active
source .venv/bin/activate 

# Run the start script (handles finding env vars from .env if needed)
./scripts/start_mcp_server.sh

This is useful for seeing direct output and logs.

Integrating with AI Clients

To use this server with AI clients like Cursor or Claude CLI, you need to register it.

Cursor Setup

Open your global MCP configuration file located at ~/.cursor/mcp.json (create it if it doesn't exist).

Add the following entry within the mcpServers object, replacing placeholders with your actual values and ensuring the path to start_mcp_server.sh is correct:

{
  "mcpServers": {
    // ... other servers ...
    "databricks-mcp-local": { 
      "command": "/absolute/path/to/your/project/databricks-mcp-server/start_mcp_server.sh",
      "args": [],
      "env": {
        "DATABRICKS_HOST": "https://your-databricks-instance.azuredatabricks.net", 
        "DATABRICKS_TOKEN": "dapiXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
        "RUNNING_VIA_CURSOR_MCP": "true" 
      }
    }
    // ... other servers ...
  }
}

Important: Replace /absolute/path/to/your/project/databricks-mcp-server/ with the actual absolute path to this project directory on your machine.
Replace the DATABRICKS_HOST and DATABRICKS_TOKEN values with your credentials.
Save the file and restart Cursor.
You can now invoke tools using databricks-mcp-local:<tool_name> (e.g., databricks-mcp-local:list_jobs).

Claude CLI Setup

Use the claude mcp add command to register the server. Provide your credentials using the -e flag for environment variables and point the command to the start_mcp_server.sh script using -- followed by the absolute path:

claude mcp add databricks-mcp-local \
  -s user \
  -e DATABRICKS_HOST="https://your-databricks-instance.azuredatabricks.net" \
  -e DATABRICKS_TOKEN="dapiXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX" \
  -- /absolute/path/to/your/project/databricks-mcp-server/start_mcp_server.sh

Important: Replace /absolute/path/to/your/project/databricks-mcp-server/ with the actual absolute path to this project directory on your machine.
Replace the DATABRICKS_HOST and DATABRICKS_TOKEN values with your credentials.
You can now invoke tools using databricks-mcp-local:<tool_name> in your Claude interactions.

Querying Databricks Resources

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

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:

# 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:

# 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.

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:

# 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.

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

License

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

About

A Model Completion Protocol (MCP) server for interacting with Databricks services. Maintained by markov.bot.

Markov Databricks 集成