What are the prerequisites for using π MCP Databricks?
You need Python 3.11 or higher, a Databricks workspace, and a Databricks Personal Access Token (PAT).
MCP Databricks
@leminkhoa
Basic information
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 π MCP Databricks?
π 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 π 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 π 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 π MCP Databricks
- Automate cluster lifecycle management via AI assistant conversation
- Run adβhoc 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
How do I install π 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 π MCP Databricks use?
The server uses the **stdio transport** for seamless compatibility with Claude Desktop and other MCP clients.
What tools does π 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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