MCP Server for Databricks Interaction

Name: MCP Server for Databricks Interaction
Author: ZubeyirOflaz

@ZubeyirOflaz

Repository

About MCP Server for Databricks Interaction

No overview available yet

Basic information

Config

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

{
  "mcpServers": {
    "mcp_server_for_databricks": {
      "command": "uv",
      "args": [
        "venv",
        "#",
        "Create",
        "a",
        "virtual",
        "environment",
        "(e.g.,",
        ".venv)"
      ]
    }
  }
}

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 Server for Databricks Interaction?

The MCP Server for Databricks Interaction acts as an interface to a Databricks workspace, exposing tools to query and retrieve metadata about schemas, tables, table samples, and job results via MCP commands. It is built with FastMCP and uses the Databricks SDK and CLI for backend communication, targeting users who need simplified metadata retrieval through an MCP interface.

How to use MCP Server for Databricks Interaction?

Install prerequisites (Python 3.x, uv, and the latest databricks-cli from the official site), clone the repo, create a virtual environment with uv venv, run uv sync, and execute python init.py to configure the workspace URL, authenticate via OAuth, select a SQL Warehouse, and set the sample size. Then add the server to an MCP client (e.g., Cursor) by specifying the uv command with arguments pointing to main.py in the project directory.

Key features of MCP Server for Databricks Interaction

Retrieves schemas and tables in a specified catalog
Returns detailed table metadata with column info and sample data
Provides schema metadata including comments and table details
Fetches results from the latest job run (or last failed run)
Uses Databricks CLI OAuth profile for authentication

Use cases of MCP Server for Databricks Interaction

Explore all schemas and tables within a catalog for data discovery
Inspect table structure, comments, and sample rows for schema understanding
Quickly retrieve the output of the most recent Databricks job run
Debug by filtering for the last failed run of a specific job

FAQ from MCP Server for Databricks Interaction

What are the prerequisites?

Python 3.x, uv (package installer), and the latest databricks-cli (version 0.18 from PyPI is too old; install directly from Databricks).

How is authentication handled?

The server uses a Databricks CLI profile named mcp_server_for_databricks. The init.py script initiates an OAuth login flow; the server obtains tokens via subprocess calls to databricks auth login and databricks auth token.

What MCP tools are provided?

Four tools: get_schemas(catalog), get_table_sample_tool(catalog, schema_name, table), get_schema_metadata(catalog_name, schema_name), and get_job_run_result(job_name, filter_for_failed_runs=False).

How is the connection configured?

The init.py script saves the workspace URL, selected SQL Warehouse ID/name, and sample size into a config.yaml file. The server reads this file on startup.

Can I use this server with Cursor IDE?

Yes. In Cursor Settings > Features > MCP, add a new server with command /path/to/uv and args including --directory /PATH/TO/THIS/REPO run main.py.

Comments

More Other MCP servers

ghidraMCP

LaurieWired

MCP Server for Ghidra

9.4K

🚀 Model Context Protocol (MCP) Curriculum for Beginners

microsoft

This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable,

16.7K