Databricks Unity Catalog MCP Server

Access your Databricks workspace through Claude and other LLMs. Query Unity Catalog tables, inspect jobs, and retrieve detailed metadata—all through the Model Context Protocol.

Built on the Databricks SDK to provide read-only access to your workspace through the Model Context Protocol. Powered by FastMCP with async/aiohttp for efficient parallel data retrieval.

Read more about our vision and use cases here.

Table of Contents

• Features

• Available Tools

  • Unity Catalog

  • Jobs

• Quick Start

  • Cursor

  • Continue.dev

• Local Development

• License

Features

Capabilities

What you can do:

• Ask Claude to find tables in your Unity Catalog

• Inspect job configurations and recent runs

• Generate queries based on your schema

Limitations

What you can't do:

• Modify tables or jobs (read-only by design)

• Execute queries directly (retrieves metadata only)

Available Tools

Unity Catalog

Jobs

Quick Start

Prerequisites:

• Docker Desktop installed and running

• Databricks workspace access (host URL and access token)

Installation

Choose your editor and follow the configuration steps:

Step 1: Add the following configuration to .cursor/mcp.json:

{
  "mcpServers": {
    "databricks": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "DATABRICKS_HOST",
        "-e",
        "DATABRICKS_TOKEN",
        "ghcr.io/revodatanl/databricks-mcp-server:latest"
      ],
      "env": {
        "DATABRICKS_HOST": "${env:DATABRICKS_HOST}",
        "DATABRICKS_TOKEN": "${env:DATABRICKS_TOKEN}"
      }
    }
  }
}

Note: You can either use environment variable references (${env:VARIABLE}) or hardcode the values as strings directly in the configuration.

Step 2: Create a .env file in your project root with your credentials:

DATABRICKS_HOST=your-workspace-url
DATABRICKS_TOKEN=your-access-token

Step 3: Restart Cursor to load the MCP server.

Step 4: Use the cursor rules to enhance your Databricks development workflow.

Learn more about MCP in Cursor

Step 1: Add the following configuration to .continue/mcpServers/databricks-mcp.yaml:

name: databricks_mcp_server
version: 0.1.3
schema: v1
mcpServers:
  - name: databricks_mcp_server
    command: docker
    args:
      - run
      - -i
      - --rm
      - -e
      - DATABRICKS_HOST=${{ inputs.DATABRICKS_HOST }}
      - -e
      - DATABRICKS_TOKEN=${{ inputs.DATABRICKS_TOKEN }}
      - ghcr.io/revodatanl/databricks-mcp-server:latest

Step 2: Set your credentials either:

• On the Continue.dev website (recommended for security)

• Or in a .env file in your project root:

  DATABRICKS_HOST=your-workspace-url
  DATABRICKS_TOKEN=your-access-token

Step 3: Restart your editor to load the MCP server.

Step 4: Use the Continue.dev rules to enhance your Databricks development workflow.

Learn more about MCP in Continue.dev

Local Development

For contributors and developers who want to run the server locally:

Setup

1. Install uv - Fast Python package installer Follow the installation guide

2. Clone the repository

   git clone https://github.com/revodatanl/databricks-mcp-server.git
   cd databricks-mcp-server

3. Install dependencies

   uv sync

4. Set environment variables

   export DATABRICKS_HOST=your-workspace-url
   export DATABRICKS_TOKEN=your-access-token

5. Run the server

   uv run databricks-mcp

License

MIT License - see LICENSE.md for details.