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MCP Databricks

@leminkhoa

Basic information

Category

Other

Transports

stdio

Author

leminkhoa

About MCP Databricks

My Databricks MCP server to interact with Databricks through LLM models

MCP Databricks config

Add this server to your MCP-compatible client using the configuration below.

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

MCP Databricks tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is πŸš€ MCP Databricks?

πŸš€ 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 πŸš€ 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 πŸš€ 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 πŸš€ 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

Frequently asked questions

1

What are the prerequisites for using πŸš€ MCP Databricks?

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

2

How do I install πŸš€ 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 πŸš€ MCP Databricks use?

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

5

What tools does πŸš€ 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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