databricks-mcp

# Databricks MCP Server This project implements a Model Context Protocol (MCP) server for interacting with Databricks using the `databricks-sdk`. ## Overview This server allows AI agents and other applications compatible with MCP to leverage Databricks functionalities, including: * Workspace management (notebooks, files, repos, secrets) * Compute management (clusters, SQL warehouses) * Data interaction (SQL execution via warehouses, catalog browsing) * AI/ML workflow management (MLflow, Model Serving, Vector Search) * Job execution & management Refer to the [Product Requirements Document](docs/databricks_mcp_prd.md) for original features and the [Technical Architecture](docs/databricks_mcp_tech_arch.md) for design specifics. For a detailed list of implemented tools and resources, see the [Capabilities Document](docs/capabilities.md). ## Setup 1. **Prerequisites:** * Python >=3.10,<3.13 (as required by the `mcp` package) * `Poetry` (>=1.2, recommend latest) * Access to a Databricks workspace * Databricks authentication configured (e.g., via environment variables `DATABRICKS_HOST` and `DATABRICKS_TOKEN`, or other methods supported by `databricks-sdk`). See [SDK Authentication](https://databricks-sdk-py.readthedocs.io/en/latest/authentication.html). 2. **Clone the repository:** ```bash git clone <repository-url> cd databricks-mcp-server ``` 3. **Install dependencies:** ```bash poetry install ``` This will create a virtual environment (if one doesn't exist) and install all dependencies specified in `pyproject.toml` and `poetry.lock`. 4. **Activate virtual environment:** ```bash poetry shell ``` ## Configuration The server is configured primarily through environment variables. Create a `.env` file in the project root by copying `.env.example` and filling in your values for local development: ```bash cp .env.example .env # Now edit .env ``` **Required `.env` Variables:** * `DATABRICKS_HOST`: Your Databricks workspace URL (e.g., `https://dbc-XXXX.cloud.databricks.com`). * `DATABRICKS_TOKEN`: Your Databricks Personal Access Token (or configure another auth method recognized by the SDK). **Optional `.env` Variables:** * `LOG_LEVEL`: Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL). Defaults to `INFO`. * `ENABLE_GET_SECRET`: Set to `true` to enable the `databricks:secrets:get_secret` tool. Defaults to `false`. **Use with extreme caution.** ## Usage Make sure your virtual environment is activated (`poetry shell`) and your `.env` file is configured. Run the server via `stdio`: ```bash python -m src.databricks_mcp ``` An MCP client/host can then connect to this process via its standard input/output. (Instructions for HTTP/SSE transport will be added if implemented). ## Development 1. **Setup:** Follow the steps in the [Setup](#setup) section. Ensure development dependencies are installed (they are by default with `poetry install`). 2. **Running Tests:** ```bash pytest ``` 3. **Linting/Formatting:** ```bash ruff check . ruff format . ``` 4. **Project Structure:** See the [Technical Architecture](docs/databricks_mcp_tech_arch.md#32-recommended-project-structure) document. 5. **Adding Tools/Resources:** * Create or modify Python files under `src/databricks_mcp/tools/` or `src/databricks_mcp/resources/`. * Implement the logic using the `mcp` framework (`@mcp.tool()`, `@mcp.resource()`) and the `db_client.py` wrapper. * Register the new capabilities in `src/databricks_mcp/server.py`. * Add corresponding unit tests in the `tests/unit/` directory. See the [Implementation Plan](docs/implementation-plan.md) for tracking development tasks. The implementation based on the initial plan is now complete.