iflow-mcp_mcp-databricks-server 0.1.0
A Model Context Protocol server that connects to Databricks API
Requires Python: >=3.7

# 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