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MCP Databricks

@leminkhoa

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

leminkhoa

关于 MCP Databricks

My Databricks MCP server to interact with Databricks through LLM models

#databricks#mcp#ai-assistant

MCP Databricks 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "databricks-mcp": {
      "command": "docker",
      "args": [
        "build",
        "-t",
        "databricks-mcp",
        "."
      ]
    }
  }
}

MCP Databricks 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is 🚀 MCP Databricks?

🚀 MCP Databricks is a Python-based MCP server that connects AI assistants (like Claude) to Databricks workspaces. It provides a rich collection of tools for managing compute resources, executing SQL queries, and organizing workspace objects via the Model Context Protocol.

How to use 🚀 MCP Databricks?

Clone the repository, create a .env file with your DATABRICKS_HOST and DATABRICKS_TOKEN, then run the server using Docker (recommended for production) or locally with uv. Configure it in Cursor via mcp.json or connect directly to any MCP client (e.g., Claude Desktop) using stdio transport.

Key features of 🚀 MCP Databricks

  • Manage Databricks clusters (create, start, delete, list)
  • Execute Python, Scala, and SQL commands on clusters
  • Install libraries (JAR, WHL, PyPI, Maven, CRAN)
  • Manage SQL warehouses (list, create)
  • Manipulate workspace objects (import, delete, create directories)

Use cases of 🚀 MCP Databricks

  • Automate cluster lifecycle management via AI assistant conversation
  • Run ad‑hoc SQL queries and analyze results in Databricks
  • Install required libraries on running clusters interactively
  • Organize and import notebooks or files into the workspace

常见问题

1

What are the prerequisites for using 🚀 MCP Databricks?

You need Python 3.11 or higher, a Databricks workspace, and a Databricks Personal Access Token (PAT).

2

How do I install 🚀 MCP Databricks?

You can either build the Docker image with `docker build -t databricks-mcp .` or install locally with `uv venv`, `uv sync`, and then run `uv run main.py`.

3

How do I configure credentials?

Create a `.env` file in the project root with `DATABRICKS_HOST` (your workspace URL) and `DATABRICKS_TOKEN` (your PAT). Optional settings include server host, port, debug mode, and log level.

4

What transport does 🚀 MCP Databricks use?

The server uses the **stdio transport** for seamless compatibility with Claude Desktop and other MCP clients.

5

What tools does 🚀 MCP Databricks provide?

It offers tools for cluster management (`list_clusters`, `create_cluster`, `delete_cluster`, `start_cluster`, `list_node_types`, `list_spark_versions`, `get_cluster`), library installation (`install_libraries`), command execution (`execute_command`, `create_execution_context`), SQL warehouse management (`list_sql_warehouses`, `create_sql_warehouse`), and workspace object management (`delete_workspace_object`, `get_workspace_object_status`, `import_workspace_object`, `create_workspace_directory`).

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