mcp-databricks-server

A Model Context Protocol (MCP) server that connects to Databricks API, allowing LLMs to run SQL queries, list jobs, and get job status.

• Run SQL queries on Databricks SQL warehouses
• List all Databricks jobs
• Get status of specific Databricks jobs
• Get detailed information about Databricks jobs

• Python 3.7+
• Databricks workspace with:
• Personal access token
• SQL warehouse endpoint
• Permissions to run queries and access jobs

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

1. Install dependencies:

   pip install -r requirements.txt

1. 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

1. Test your connection (optional but recommended):

   python test_connection.py

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

1. HTTP Path: For your SQL warehouse:

• Go to SQL Warehouses in Databricks
• Select your warehouse
• Find the connection details and copy the HTTP Path

Start the MCP server:

python main.py

You can test the MCP server using the inspector by running

npx @modelcontextprotocol/inspector python3 main.py

The following MCP tools are available:

1. run_sql_query(sql: str) - Execute SQL queries on your Databricks