Databricks

by jordineil

21 stars

503 downloads

Not rated

GitHub

About

Bridges Claude with Databricks environments, enabling natural language interaction with SQL warehouses and job management systems through FastAPI-based tools for running queries, listing jobs, and retrieving detailed information.

Details

Author: jordineil
Repository: JordiNeil/mcp-databricks-server
GitHub stars: 21
Downloads: 503
Categories: Developer Tools, Design, Workplace, AI, Search, Knowledge Base, API, Infrastructure, Other

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. 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

Start the MCP server:

python main.py

You can test the MCP server using the inspector by running

npx @modelcontextprotocol/inspector python3 main.py

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"

`run_sql_query`

Execute SQL queries on your Databricks SQL warehouse. Parameter: sql (string)

`list_jobs`

List all Databricks jobs in your workspace.

`get_job_status`

Get the status of a specific Databricks job by ID. Parameter: job_id (integer)

`get_job_details`

Get detailed information about a specific Databricks job. Parameter: job_id (integer)

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 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

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About

Details

Jump to

Setting up with Highlight

From the repository

run_sql_query

list_jobs

get_job_status

get_job_details

Claude Desktop / Cursor

Linux

Macos

Windows

Databricks MCP Server

Features

Prerequisites

Setup

Obtaining Databricks Credentials

Running the Server

Available MCP Tools

Example Usage with LLMs

Troubleshooting

Connection Issues

Security Considerations

Install

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