Databricks Unity Catalog MCP Server

Databricks Unity Catalog MCP Server

Access your Databricks workspace through Claude and other LLMs. Query Unity Catalog tables, inspect jobs, and retrieve detailed metadata.

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Databricks Unity Catalog MCP Server

License: MIT Python 3.11+ Docker

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

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

Tool Description Parameters
get-all-catalogs-schemas-tables List all tables across catalogs and schemas None
get-table-details Retrieve table descriptions, columns, and metadata full_table_names (list of catalog.schema.table)

Jobs

Tool Description Parameters
get-jobs List all workspace jobs with IDs and names None
get-job-details Get job settings, configurations, and tasks job_ids (list of job IDs)
get-job-runs Fetch recent run history with duration, parameters, and results job_ids (list), n_recent (1-5, default: 1)

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:

<details> <summary><strong>Cursor</strong></summary>

<br>

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

</details>

<details> <summary><strong>Continue.dev</strong></summary>

<br>

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

</details>


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.

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