databricks-mcp

# 🚀 MCP Databricks <div align="center"> <img src="https://img.shields.io/badge/python-3.11+-blue.svg" alt="Python 3.11+"> <img src="https://img.shields.io/badge/platform-Databricks-orange.svg" alt="Databricks"> <img src="https://img.shields.io/badge/MCP-Protocol-green.svg" alt="MCP Protocol"> </div> <p align="center"> <b>A powerful Databricks integration for AI assistants via Model Context Protocol</b> </p> ## 📖 Introduction MCP Databricks seamlessly connects AI assistants to your Databricks workspaces through the Model Context Protocol (MCP). Built with Python, it provides a rich collection of tools for managing virtually every aspect of your Databricks environment. With this server, AI assistants like Claude can: - 🔧 Manage compute resources with precision - 📊 Execute SQL queries and analyze results - 📁 Organize and manipulate workspace objects - ✨ And much more! ## 🔍 Prerequisites - 🐍 Python 3.11 or higher - 💻 A Databricks workspace - 🔑 Databricks Personal Access Token (PAT) - 📦 Required Python packages (installed in setup) ## 🚀 Quickstart ### 1️⃣ Clone the repository ```bash git clone https://github.com/leminkhoa/databricks-mcp cd databricks-mcp ``` ### 2️⃣ Configure environment variables Create a `.env` file in the project root with your Databricks credentials: ```ini # Databricks API Configuration DATABRICKS_HOST="https://adb-<your workspace uri>.azuredatabricks.net/" DATABRICKS_TOKEN="dapi_<your_token_here>" # Server Configuration SERVER_HOST="0.0.0.0" SERVER_PORT="8000" DEBUG="false" TRANSPORT="stdio" # Logging Configuration LOG_LEVEL="INFO" ``` > 💡 **Tip:** For further instructions, You can use the `env.sample` file as a template. ### 3️⃣ Choose Your Installation Method You can use MCP Databricks in two ways: #### Option A: Docker (Recommended for production) 1. Build the Docker image: ```bash docker build -t databricks-mcp . ``` 2. Configure in Cursor with the following `mcp.json` entry: ```json { "databricks-mcp-docker": { "command": "docker", "args": [ "run", "--rm", "-i", "--name", "databricks-mcp", "--env-file", "<path/to/.env>", "databricks-mcp" ] } } ``` > 💡 **Note:** Replace `<path/to/.env>` with the absolute path to your `.env` file. #### Option B: Local Installation with uv 1. Install uv (if not already installed): ```bash curl -LsSf https://astral.sh/uv/install.sh | sh ``` > 💡 **Note:** See [uv installation documentation](https://docs.astral.sh/uv/getting-started/installation/#standalone-installer) for alternative installation methods. 2. Set up virtual environment: ```bash uv venv source .venv/bin/activate # On Windows: .venv\Scripts\activate ``` 3. Install dependencies with uv: ```bash uv sync ``` 4. Configure in Cursor with the following `mcp.json` entry: ```json { "databricks-mcp-stdio": { "command": "uv", "args": [ "--directory", "<repository directory>", "run", "main.py" ] } } ``` > 💡 **Note:** Replace `<repository directory>` with the absolute path to your cloned repository. ## 🚀 Usage ### Running the MCP Server If not using Docker or Cursor integration, start the server with: ```bash python main.py ``` or ```bash uv run main.py ``` ### Connecting to Claude or other MCP clients This server uses the **stdio transport** for seamless compatibility with Claude Desktop and other MCP clients. After installing the server, you can immediately connect to it using your preferred MCP client. ## 🧰 Tools and Capabilities The MCP Databricks server provides a comprehensive toolkit for managing your Databricks environment: ### 💻 Cluster Management | Tool | Description | |---------------------|-----------------------------------------------------------------------------| | `list_clusters` | List all Databricks clusters in the workspace | | `create_cluster` | Create a new Databricks cluster with customizable settings | | `delete_cluster` | Delete a Databricks cluster by ID | | `start_cluster` | Start a terminated Databricks cluster | | `list_node_types` | List all available node types for Databricks clusters | | `list_spark_versions` | List all available Spark versions for Databricks clusters | | `get_cluster` | Get detailed information about a specific Databricks cluster | ### 📦 Library Management | Tool | Description | |---------------------|-----------------------------------------------------------------------------| | `install_libraries` | Install libraries (JAR, WHL, PyPI, Maven, CRAN, etc.) on a running cluster | ### 🖥️ Command Execution | Tool | Description | |-------------------------|------------------------------------------------------------------------| | `execute_command` | Execute a command (Python, Scala, SQL) on a running Databricks cluster | | `create_execution_context` | Create an execution context for interactive command sessions | ### 📊 SQL Warehouse Management | Tool | Description | |-------------------------|------------------------------------------------------------------------| | `list_sql_warehouses` | List all SQL warehouses in the workspace | | `create_sql_warehouse` | Create a new SQL warehouse with configurable size and settings | ### 📁 Workspace Objects | Tool | Description | |-----------------------------|--------------------------------------------------------------------| | `delete_workspace_object` | Delete an object from the Databricks workspace | | `get_workspace_object_status` | Get the status of an object in the Databricks workspace | | `import_workspace_object` | Import an object (notebook, file, etc.) into the workspace | | `create_workspace_directory` | Create a directory in the Databricks workspace | ---