Databricks MCP Server

# Databricks MCP Server A Model Context Protocol (MCP) server that connects to Databricks API, allowing LLMs to run SQL queries, list jobs, and get job status. ## Features - Run SQL queries on Databricks SQL warehouses - List all Databricks jobs - Get status of specific Databricks jobs - Get detailed information about Databricks jobs ## Prerequisites - Python 3.7+ - Databricks workspace with: - Personal access token - SQL warehouse endpoint - Permissions to run queries and access jobs ## Setup 1. Clone this repository 2. Create and activate a virtual environment (recommended): ``` python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate ``` 3. Install dependencies: ``` pip install -r requirements.txt ``` 4. Create a `.env` file in the root directory with the following variables: ``` DATABRICKS_HOST=your-databricks-instance.cloud.databricks.com DATABRICKS_TOKEN=your-personal-access-token DATABRICKS_HTTP_PATH=/sql/1.0/warehouses/your-warehouse-id ``` 5. Test your connection (optional but recommended): ``` python test_connection.py ``` ### Obtaining Databricks Credentials 1. **Host**: Your Databricks instance URL (e.g., `your-instance.cloud.databricks.com`) 2. **Token**: Create a personal access token in Databricks: - Go to User Settings (click your username in the top right) - Select "Developer" tab - Click "Manage" under "Access tokens" - Generate a new token, and save it immediately 3. **HTTP Path**: For your SQL warehouse: - Go to SQL Warehouses in Databricks - Select your warehouse - Find the connection details and copy the HTTP Path ## Running the Server Start the MCP server: ``` python main.py ``` You can test the MCP server using the inspector by running ``` npx @modelcontextprotocol/inspector python3 main.py ``` ## Available MCP Tools The following MCP tools are available: 1. **run_sql_query(sql: str)** - Execute SQL queries on your Databricks SQL warehouse 2. **list_jobs()** - List all Databricks jobs in your workspace 3. **get_job_status(job_id: int)** - Get the status of a specific Databricks job by ID 4. **get_job_details(job_id: int)** - Get detailed information about a specific Databricks job ## Example Usage with LLMs When used with LLMs that support the MCP protocol, this server enables natural language interaction with your Databricks environment: - "Show me all tables in the database" - "Run a query to count records in the customer table" - "List all my Databricks jobs" - "Check the status of job #123" - "Show me details about job #456" ## Troubleshooting ### Connection Issues - Ensure your Databricks host is correct and doesn't include `https://` prefix - Check that your SQL warehouse is running and accessible - Verify your personal access token has the necessary permissions - Run the included test script: `python test_connection.py` ## Security Considerations - Your Databricks personal access token provides direct access to your workspace - Secure your `.env` file and never commit it to version control - Consider using Databricks token with appropriate permission scopes only - Run this server in a secure environment