databricks-mcp-server by revodatanl - 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—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 .

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

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:

Cursor

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 to enhance your Databricks development workflow.

Learn more about MCP in Cursor

Continue.dev

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 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

Install uv - Fast Python package installer Follow the installation guide

Clone the repository

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

Install dependencies
```
uv sync
```

Set environment variables

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

Run the server
```
uv run databricks-mcp
```

License

MIT License - see for details.