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MCP Databricks

@leminkhoa

リポジトリ

基本情報

カテゴリ

その他

トランスポート

stdio

作者

leminkhoa

MCP Databricks について

My Databricks MCP server to interact with Databricks through LLM models

#databricks#mcp#ai-assistant

MCP Databricks の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

MCP Databricks のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

よくある質問

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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