🚀 MCP Databricks

Name: 🚀 MCP Databricks
Author: leminkhoa

@leminkhoa

Repository

About 🚀 MCP Databricks

My Databricks MCP server to interact with Databricks through LLM models

#databricks #mcp #ai-assistant

Basic information

Config

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

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

Tools

No tools detected

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

Overview

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

FAQ from 🚀 MCP Databricks

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).

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.

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.

What transport does 🚀 MCP Databricks use?

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

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).

Comments

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