MCP Server for Databricks Interaction

Name: MCP Server for Databricks Interaction
Author: ZubeyirOflaz

@ZubeyirOflaz

代码仓库

关于 MCP Server for Databricks Interaction

暂无概览

基本信息

分类

其他

许可证

LGPL-2.1 license

运行时

python

传输方式

stdio

发布者

ZubeyirOflaz

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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.