Content
# Genie MCP Server
This project provides an async server for interacting with Databricks Genie via a MCP StreamableHTTP Protocol. It enables users to query Genie and receive answers or data, leveraging Databricks authentication and robust error handling.
## Features
- Async API server using Starlette and Uvicorn
- Integration with Databricks Genie for conversational queries
- Automatic OAuth token management for Databricks
## Project Structure
- `mcp_server.py`: Main server entry point, exposes the tool via MCP StreamableHTTP Protocol.
- `genie_room.py`: Handles Genie API interactions and conversation logic.
- `token_minter.py`: Manages Databricks OAuth token minting and refreshing.
- `requirements.txt`: Python dependencies.
- `app.yaml`: Example deployment configuration.
## Deploying to Databricks Apps
You can deploy the Genie MCP Server as a Databricks app by following these steps:
1. **Clone the Repository to Your Workspace**
In your Databricks workspace, navigate to the directory where you want to deploy the app (e.g., `/Workspace/Users/your.email@databricks.com/genie_mcp_server`). Then, clone the repository:
```bash
git clone https://github.com/your-org/genie_mcp_server.git
```
2. **Configure the Genie Space ID and Other Environment Variables**
Open the `app.yaml` file in the root of the cloned repository. Update the `SPACE_ID` value to match your Genie space.
Example `app.yaml`:
```yaml
command:
- "python"
- "mcp_server.py"
env:
- name: "SPACE_ID"
value: "your_space_id"
```
3. **Create and Deploy the App in Databricks**
- Go to the Databricks Apps interface.
- Create a new app and specify the path to the directory where you cloned the repository.
- Complete the app creation and deployment process.
```databricks apps deploy genie-mcp-server --source-code-path /Workspace/Users/your.email@databricks.com/genie_mcp_server```
4. **Access the API**
Once deployed, your Genie MCP Server app will be running and accessible via the configured port and endpoint (e.g., `https://app-url.aws.databricksapps.com/api/mcp/`). You can now send MCP requests to your deployed app from within your Databricks workspace or from external clients, depending on your network configuration.
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