Databricks

by justtryai

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GitHub

About

Integrates with Databricks services to enable interaction with clusters, jobs, notebooks, DBFS, and SQL workspaces via tools that wrap the Databricks REST API.

Details

Author: justtryai
Repository: JustTryAI/databricks-mcp-server
GitHub stars: 16
Categories: Productivity, Developer Tools, Design, Workplace, AI, Infrastructure, Database, Automation
Tags: #integration

- MCP Protocol Support: Implements the MCP protocol to allow LLMs to interact with Databricks
- Databricks API Integration: Provides access to Databricks REST API functionality
- Tool Registration: Exposes Databricks functionality as MCP tools
- Async Support: Built with asyncio for efficient operation

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below
Plugin Name Databricks

Command (node, npx, python, etc.) npx
Arguments
- Argument 1 -y
- Argument 2 @highlight/mcp-server
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "databricks": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "databricks": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "databricks": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "databricks": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "databricks": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "databricks": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "databricks": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "databricks": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "databricks": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "databricks": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "databricks": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "databricks": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "databricks": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "databricks": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "databricks": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

From the repository

1. Install uv if you don't have it already:


uv venv
uv pip install -e .
uv pip install -e ".[dev]"

4. Set up environment variables:

To start the MCP server, run:

bash

`list_clusters`

List all Databricks clusters.

`create_cluster`

Create a new Databricks cluster.

`terminate_cluster`

Terminate a Databricks cluster.

`get_cluster`

Get information about a specific Databricks cluster.

`start_cluster`

Start a terminated Databricks cluster.

`list_jobs`

List all Databricks jobs.

`run_job`

Run a Databricks job.

`list_notebooks`

List notebooks in a workspace directory.

`export_notebook`

Export a notebook from the workspace.

`list_files`

List files and directories in a DBFS path.

`execute_sql`

Execute a SQL statement.

The Databricks MCP Server exposes the following tools:

- list_clusters: List all Databricks clusters
- create_cluster: Create a new Databricks cluster
- terminate_cluster: Terminate a Databricks cluster
- get_cluster: Get information about a specific Databricks cluster
- start_cluster: Start a terminated Databricks cluster
- list_jobs: List all Databricks jobs
- run_job: Run a Databricks job
- list_notebooks: List notebooks in a workspace directory
- export_notebook: Export a notebook from the workspace
- list_files: List files and directories in a DBFS path
- execute_sql: Execute a SQL statement

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "databricks": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Linux

{
    "env": [],
    "args": [
        "-y",
        "@highlight/mcp-server"
    ],
    "command": "npx"
}

Macos

{
    "env": [],
    "args": [
        "-y",
        "@highlight/mcp-server"
    ],
    "command": "npx"
}

Windows

{
    "env": [],
    "args": [
        "/c",
        "npx",
        "-y",
        "@highlight/mcp-server"
    ],
    "command": "cmd"
}

Databricks MCP Server

A Model Completion Protocol (MCP) server for Databricks that provides access to Databricks functionality via the MCP protocol. This allows LLM-powered tools to interact with Databricks clusters, jobs, notebooks, and more.

Features

Available Tools

The Databricks MCP Server exposes the following tools:

Installation

Prerequisites

- Python 3.10 or higher
- uv package manager (recommended for MCP servers)

Setup

1. Install uv if you don't have it already:

   # MacOS/Linux
   curl -LsSf https://astral.sh/uv/install.sh | sh
   
   # Windows (in PowerShell)
   irm https://astral.sh/uv/install.ps1 | iex

Restart your terminal after installation.

2. Clone the repository:

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

   cd databricks-mcp-server

3. Set up the project with uv:

   # Create and activate virtual environment

   uv venv

   

   # On Windows

   .\.venv\Scripts\activate

   

   # On Linux/Mac

   source .venv/bin/activate

   

   # Install dependencies in development mode

   uv pip install -e .

   

   # Install development dependencies

   uv pip install -e ".[dev]"

4. Set up environment variables:

   # Windows

   set DATABRICKS_HOST=https://your-databricks-instance.azuredatabricks.net

   set DATABRICKS_TOKEN=your-personal-access-token

   

   # Linux/Mac

   export DATABRICKS_HOST=https://your-databricks-instance.azuredatabricks.net

   export DATABRICKS_TOKEN=your-personal-access-token

You can also create an .env file based on the .env.example template.

Running the MCP Server

To start the MCP server, run:

```bash

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About

Details

Jump to

Setting up with Highlight

From the repository

list_clusters

create_cluster

terminate_cluster

get_cluster

start_cluster

list_jobs

run_job

list_notebooks

export_notebook

list_files

execute_sql

Claude Desktop / Cursor

Linux

Macos

Windows

Databricks MCP Server

Features

Available Tools

Installation

Prerequisites

Setup

Running the MCP Server

Install

More like this

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Github

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Context 7

`list_clusters`

`create_cluster`

`terminate_cluster`

`get_cluster`

`start_cluster`

`list_jobs`

`run_job`

`list_notebooks`

`export_notebook`

`list_files`

`execute_sql`