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:

   # 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.

Running the MCP Server

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

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/             

... [View full README on GitHub](https://github.com/JustTryAI/databricks-mcp-server#readme)