Databricks

What is mcp-databricks-server?

mcp-databricks-server is a Model Context Protocol (MCP) server designed to connect Large Language Models (LLMs) with the Databricks API. This allows LLMs to interact with Databricks for tasks such as running SQL queries, listing jobs, and retrieving job statuses.

How to use mcp-databricks-server?

To use mcp-databricks-server, you need Python 3.7+ and a Databricks workspace with a personal access token, a SQL warehouse endpoint, and appropriate permissions. After cloning the repository, set up a virtual environment, install dependencies from requirements.txt, and configure a .env file with your Databricks host, token, and HTTP path. You can then run the server using python main.py and test it with the MCP inspector.

Key Features of mcp-databricks-server

• Run SQL queries: Execute SQL queries on Databricks SQL warehouses.
• List Databricks jobs: Retrieve a list of all Databricks jobs in your workspace.
• Get job status: Obtain the status of specific Databricks jobs by ID.
• Get job details: Access detailed information about Databricks jobs.

Use Cases of mcp-databricks-server

This project enables natural language interaction with your Databricks environment through LLMs. Example use cases include:

• "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"

FAQ about mcp-databricks-server

Q: What are the prerequisites for running this server? A: Python 3.7+, a Databricks workspace with a personal access token, a SQL warehouse endpoint, and permissions to run queries and access jobs.

Q: How do I obtain Databricks credentials? A: Your Databricks instance URL is the host. A personal access token can be generated in your Databricks User Settings under the "Developer" tab. The HTTP Path for your SQL warehouse can be found in its connection details.

Q: What if I encounter connection issues? A: Ensure your Databricks host is correct (without https://), your SQL warehouse is running, your personal access token has necessary permissions, and try running the test_connection.py script.

Q: What are the security considerations? A: Your Databricks personal access token provides direct access. Secure your .env file and never commit it to version control. Consider using tokens with appropriate permission scopes and running the server in a secure environment.

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