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🤖 Databricks Custom MCP Demo

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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.

Credit for the initial version goes to @JustTryAI and Markov

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:

Cluster Management

• 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

Job Management

• list_jobs: List all Databricks jobs
• run_job: Run a Databricks job
• run_notebook: Submit and wait for a one-time notebook run
• create_job: Create a new Databricks job
• delete_job: Delete a Databricks job
• get_run_status: Get status information for a job run
• list_job_runs: List recent runs for a job
• cancel_run: Cancel a running job

Workspace Files

• list_notebooks: List notebooks in a workspace directory
• export_notebook: Export a notebook from the workspace
• import_notebook: Import a notebook into the workspace
• delete_workspace_object: Delete a notebook or directory
• get_workspace_file_content: Retrieve content of any workspace file (JSON, notebooks, scripts, etc.)
• get_workspace_file_info: Get metadata about workspace files

File System

• list_files: List files and directories in a DBFS path
• dbfs_put: Upload a small file to DBFS
• dbfs_delete: Delete a DBFS file or directory

Cluster Libraries

• install_library: Install libraries on a cluster
• uninstall_library: Remove libraries from a cluster
• list_cluster_libraries: Check installed libraries on a cluster

Repos

• create_repo: Clone a Git repository
• update_repo: Update an existing repo
• list_repos: List repos in the workspace
• pull_repo: Pull the latest commit for a Databricks repo

Unity Catalog

• list_catalogs: List catalogs
• create_catalog: Create a catalog
• list_schemas: List schemas in a catalog
• create_schema: Create a schema
• list_tables: List tables in a schema
• create_table: Execute a CREATE TABLE statement
• get_table_lineage: Fetch lineage information for a table

Composite

• sync_repo_and_run_notebook: Pull a repo and execute a notebook in one call

SQL Execution

• execute_sql: Execute a SQL statement (warehouse_id optional if DATABRICKS_WAREHOUSE_ID env var is set)

Manual 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/robkisk/databricks-mcp.git
   cd databricks-mcp
   

3. Run the setup script:

   # Linux/Mac
   ./scripts/setup.sh
   
   # Windows (PowerShell)
   .\scripts\setup.ps1
   

   The setup script will:

   • Install uv if not already installed
   • Create a virtual environment
   • Install all project dependencies
   • Verify the installation works

   Alternative manual setup:

   # 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:

   # Required 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
   
   # Optional: Set default SQL warehouse (makes warehouse_id optional in execute_sql)
   export DATABRICKS_WAREHOUSE_ID=sql_warehouse_12345
   

   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

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

2. 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",
           "DATABRICKS_WAREHOUSE_ID": "sql_warehouse_12345",
           "RUNNING_VIA_CURSOR_MCP": "true" 
         }
       }
       // ... other servers ...
     }
   }
   

3. Important: Replace /absolute/path/to/your/project/databricks-mcp-server/ with the actual absolute path to this project directory on your machine.

4. Replace the DATABRICKS_HOST and DATABRICKS_TOKEN values with your credentials.

5. Save the file and restart Cursor.

6. You can now invoke tools using databricks-mcp-local:<tool_name> (e.g., databricks-mcp-local:list_jobs).

Claude CLI Setup

1. 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" \
     -e DATABRICKS_WAREHOUSE_ID="sql_warehouse_12345" \
     -- /absolute/path/to/your/project/databricks-mcp-server/start_mcp_server.sh
   

2. Important: Replace /absolute/path/to/your/project/databricks-mcp-server/ with the actual absolute path to this project directory on your machine.

3. Replace the DATABRICKS_HOST and DATABRICKS_TOKEN values with your credentials.

4. 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

Usage Examples

SQL Execution with Default Warehouse

# With DATABRICKS_WAREHOUSE_ID set, warehouse_id is optional
await session.call_tool("execute_sql", {
    "statement": "SELECT * FROM my_table LIMIT 10"
})

# You can still override the default warehouse
await session.call_tool("execute_sql", {
    "statement": "SELECT * FROM my_table LIMIT 10",
    "warehouse_id": "sql_warehouse_specific"
})

Workspace File Content Retrieval

# Get JSON file content from workspace
await session.call_tool("get_workspace_file_content", {
    "workspace_path": "/Users/user@domain.com/config/settings.json"
})

# Get notebook content in Jupyter format
await session.call_tool("get_workspace_file_content", {
    "workspace_path": "/Users/user@domain.com/my_notebook",
    "format": "JUPYTER"
})

# Get file metadata without downloading content
await session.call_tool("get_workspace_file_info", {
    "workspace_path": "/Users/user@domain.com/large_file.py"
})

Repo Sync and Notebook Execution

await session.call_tool("sync_repo_and_run_notebook", {
    "repo_id": 123,
    "notebook_path": "/Repos/user/project/run_me"
})

Create Nightly ETL Job

job_conf = {
    "name": "Nightly ETL",
    "tasks": [
        {
            "task_key": "etl",
            "notebook_task": {"notebook_path": "/Repos/me/etl.py"},
            "existing_cluster_id": "abc-123"
        }
    ]
}
await session.call_tool("create_job", job_conf)

Project Structure

databricks-mcp/
├── databricks_mcp/                  # Main package (renamed from src/)
│   ├── __init__.py                  # Package initialization
│   ├── __main__.py                  # Main entry point for the package
│   ├── main.py                      # Entry point for the MCP server
│   ├── api/                         # Databricks API clients
│   │   ├── clusters.py              # Cluster management
│   │   ├── jobs.py                  # Job management
│   │   ├── notebooks.py             # Notebook operations
│   │   ├── sql.py                   # SQL execution
│   │   └── dbfs.py                  # DBFS operations
│   ├── core/                        # Core functionality
│   │   ├── config.py                # Configuration management
│   │   ├── auth.py                  # Authentication
│   │   └── utils.py                 # Utilities
│   ├── server/                      # Server implementation
│   │   ├── __main__.py              # Server entry point
│   │   ├── databricks_mcp_server.py # Main MCP server
│   │   └── app.py                   # FastAPI app for tests
│   └── cli/                         # Command-line interface
│       └── commands.py              # CLI commands
├── tests/                           # Test directory
│   ├── test_clusters.py             # Cluster tests
│   ├── test_mcp_server.py           # Server tests
│   └── test_*.py                    # Other test files
├── scripts/                         # Helper scripts (organized)
│   ├── start_mcp_server.ps1         # Server startup script (Windows)
│   ├── start_mcp_server.sh          # Server startup script (Unix)
│   ├── run_tests.ps1                # Test runner script (Windows)
│   ├── run_tests.sh                 # Test runner script (Unix)
│   ├── setup.ps1                    # Setup script (Windows)
│   ├── setup.sh                     # Setup script (Unix)
│   ├── show_clusters.py             # Script to show clusters
│   ├── show_notebooks.py            # Script to show notebooks
│   ├── setup_codespaces.sh          # Codespaces setup
│   └── test_setup_local.sh          # Local test setup
├── examples/                        # Example usage
│   ├── direct_usage.py              # Direct usage examples
│   └── mcp_client_usage.py          # MCP client examples
├── docs/                            # Documentation (organized)
│   ├── AGENTS.md                    # Agent documentation
│   ├── project_structure.md         # Detailed structure docs
│   ├── new_features.md              # Feature documentation
│   └── phase1.md                    # Development phases
├── .gitignore                       # Git ignore rules
├── .cursor.json                     # Cursor configuration
├── pyproject.toml                   # Package configuration
├── uv.lock                          # Dependency lock file
└── README.md                        # This file

See docs/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 databricks_mcp/ tests/
uv run flake8 databricks_mcp/ tests/
uv run mypy databricks_mcp/

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=databricks_mcp 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.

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