Databricks MCP Server

# Databricks MCP Server Examples This directory contains examples of how to use the Databricks MCP server. ## Example Files 1. **Direct Usage (direct_usage.py)** This example shows how to directly instantiate and use the Databricks MCP server without going through the MCP protocol. It demonstrates: - Creating a server instance - Calling tools directly - Processing the results To run this example: ```bash uv run examples/direct_usage.py ``` 2. **MCP Client Usage (mcp_client_usage.py)** This example shows how to use the MCP client to connect to the Databricks MCP server and call its tools through the MCP protocol. It demonstrates: - Connecting to the server using the MCP protocol - Listing available tools - Calling tools through the MCP protocol - Processing the results To run this example: ```bash uv run examples/mcp_client_usage.py ``` ## Running Examples Make sure you have the following prerequisites: 1. Python 3.10+ installed 2. `uv` package manager installed (see project README for installation instructions) 3. Project environment set up with `uv venv` 4. Dependencies installed with `uv add` 5. Environment variables set (DATABRICKS_HOST, DATABRICKS_TOKEN) First, make sure you're in the project root directory and the virtual environment is activated: ```bash # Windows .\.venv\Scripts\activate # Linux/Mac source .venv/bin/activate ``` Then you can run the examples as shown above. ## Example Outputs ### Direct Usage Example Output ``` Databricks MCP Server - Direct Usage Example =========================================== Databricks Clusters: ==================== Cluster 1: ID: 0220-221815-kzacbcps Name: Lloyd Burley's Cluster LTS State: TERMINATED Spark Version: 15.4.x-scala2.12 Node Type: Standard_DS3_v2 Databricks Notebooks in /: ================================ Notebook: /Shared/example_notebook Directory: /Users/ Databricks Jobs: ================ Job 1: ID: 12345 Name: Daily ETL Job Created: 1740089895875 ``` ### MCP Client Usage Example Output ``` Databricks MCP Server - MCP Client Usage Example ============================================= 2025-03-13 10:05:23,456 - __main__ - INFO - Connecting to Databricks MCP server... 2025-03-13 10:05:23,457 - __main__ - INFO - Launching server process... 2025-03-13 10:05:23,789 - __main__ - INFO - Server launched, creating session... 2025-03-13 10:05:23,790 - __main__ - INFO - Initializing session... Available Tools: ================ - list_clusters: List all Databricks clusters - create_cluster: Create a new Databricks cluster with parameters: cluster_name (required), spark_version (required), node_type_id (required), num_workers, autotermination_minutes - terminate_cluster: Terminate a Databricks cluster with parameter: cluster_id (required) - get_cluster: Get information about a specific Databricks cluster with parameter: cluster_id (required) - start_cluster: Start a terminated Databricks cluster with parameter: cluster_id (required) - list_jobs: List all Databricks jobs - run_job: Run a Databricks job with parameters: job_id (required), notebook_params (optional) - list_notebooks: List notebooks in a workspace directory with parameter: path (required) - export_notebook: Export a notebook from the workspace with parameters: path (required), format (optional, one of: SOURCE, HTML, JUPYTER, DBC) - list_files: List files and directories in a DBFS path with parameter: dbfs_path (required) - execute_sql: Execute a SQL statement with parameters: statement (required), warehouse_id (required), catalog (optional), schema (optional) Select a tool to run (or 'quit' to exit): 1. list_clusters 2. create_cluster 3. terminate_cluster 4. get_cluster 5. start_cluster 6. list_jobs 7. run_job 8. list_notebooks 9. export_notebook 10. list_files 11. execute_sql ```