Databricks MCP Server

Databricks MCP Server

Expose Databricks data and jobs securely with Model Context Protocol for LLMs.

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Databricks MCP Server implements the Model Context Protocol (MCP) to provide a bridge between Databricks APIs and large language models. It enables LLMs to run SQL queries, list Databricks jobs, retrieve job statuses, and fetch detailed job information via a standardized MCP interface. The server handles authentication, secure environment configuration, and provides accessible endpoints for interaction with Databricks workspaces.

Key Features

Run SQL queries on Databricks SQL warehouses
List all Databricks jobs in a workspace
Retrieve status of specific Databricks jobs
Obtain detailed information about Databricks jobs
MCP protocol compliance
LLM-compatible tool interface
Environment-based configuration via .env
Secure authentication using personal access tokens
Testing utilities for connection verification
Easy server start and inspection commands

Use Cases

Enabling LLM-driven data analysis in Databricks environments
Automated monitoring and reporting of Databricks job statuses
Executing natural language-triggered SQL queries on Databricks warehouses
Integrating Databricks job management with conversational AI systems
Providing secure, structured access to Databricks resources for AI tools
Custom dashboards for job and data management via LLMs
Facilitating low-code/no-code interfaces to Databricks operations
Prototyping LLM-based data pipeline management solutions
Teaching and demonstration tools for Databricks and LLM integration
Rapid development of MCP-compliant Databricks agents

README

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

Star History

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Repository Owner

JordiNeil
JordiNeil

User

Repository Details

Language Python
Default Branch main
Size 21 KB
Contributors 1
MCP Verified Nov 12, 2025

Programming Languages

Python
100%

Tags

mcp server

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