examples

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:

   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:

   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:

# 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