Databricks MCP Server

🚀 Databricks MCP Server

A Model Context Protocol (MCP) server that connects to the Databricks API, enabling Large Language Models (LLMs) to run SQL queries, list jobs, and retrieve job statuses.

✨ Features

• Run SQL queries on a Databricks SQL warehouse.
• List all Databricks jobs.
• Get the status of a specific Databricks job.
• Obtain detailed information about a specific Databricks job.

📦 Prerequisites

• Python 3.7+
• A Databricks workspace with the following configurations:
  • A personal access token.
  • An SQL warehouse endpoint.
  • Permissions to run queries and access jobs.

📦 Installation

Clone the Repository

1. Clone this repository.

Create and Activate a Virtual Environment (Recommended)

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Install Dependencies

pip install -r requirements.txt

Create an .env File

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

Test Your Connection (Optional but Recommended)

python test_connection.py

Obtain 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 on your username in the top-right corner).
   • Select the "Developers" 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.

💻 Usage Examples

Start the MCP Server

python main.py

Test the MCP Server

You can test this 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 an SQL query on your Databricks SQL warehouse.
2. list_jobs() - List all Databricks jobs in the workspace.
3. get_job_status(job_id: int) - Get the status of a specific Databricks job by its job ID.
4. get_job_details(job_id: int) - Obtain detailed information about a specific Databricks job.

Example Usage with Large Language Models

When used with Large Language Models that support the MCP protocol, this server allows you to interact with your Databricks environment in natural language:

• "Show all tables in the database."
• "Run a query to see the number of records in the customers table."
• "List all my Databricks jobs."
• "Check the status of job #123."
• "Show details of job #456."

📚 Documentation

Troubleshooting Connection Issues

• Ensure your Databricks host is correct and does not include the https:// prefix.
• Check that your SQL warehouse is running and accessible.
• Verify that 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.
• Ensure your .env file is secure and never committed to version control.
• Consider using a Databricks token with an appropriate permission scope.
• Run this server in a secure environment.