{"repo":"RafaelCartenet/mcp-databricks-server","free":true,"listed":false,"github":"https://github.com/RafaelCartenet/mcp-databricks-server","clone":"git clone https://github.com/RafaelCartenet/mcp-databricks-server.git","description":"Model Context Protocol (MCP) server for Databricks that empowers AI agents to autonomously interact with Unity Catalog metadata. Enables data discovery, lineage analysis, and intelligent SQL execution. Agents explore catalogs/schemas/tables, understand relationships, discover notebooks/jobs, and execute queries - greatly reducing ad-hoc query time.","language":"Python","stars":42,"topics":["databricks","llm","mcp","unitycatalog"],"license":"MIT","category":"mcp-servers","readme_excerpt":"Databricks MCP Server - Motivation - Overview - Practical Benefits of UC Metadata for AI Agents - Available Tools and Features - Setup - System Requirements - Installation - Permissions Requirements - Running the Server - Standalone Mode - Using with Cursor - Example Usage Workflow (for an LLM Agent) - Managing Metadata as Code with Terraform - Handling Long-Running Queries - Dependencies Motivation Databricks Unity Catalog (UC) allows for detailed documentation of your data assets, including catalogs, schemas, tables, and columns. Documenting these assets thoroughly requires an investment of time. One common question is: what are the practical benefits of this detailed metadata entry? This MCP server provides a strong justification for that effort. It enables Large Language Models (LLMs) to directly access and utilize this Unity Catalog metadata. The more comprehensively your data is described in UC, the more effectively an LLM agent can understand your Databricks environment. This deeper understanding is crucial for the agent to autonomously construct more intelligent and accurate SQL queries to fulfill data requests. Overview This Model Context Protocol (MCP) server is designed to interact with Databricks, with a strong focus on leveraging Unity Catalog (UC) metadata and enabling comprehensive data lineage exploration. The primary goal is to equip an AI agent with a comprehensive set of tools, enabling it to become independent in answering questions about your data. By aut","default_branch":null,"files":null,"tree":[],"storefront":"/r/RafaelCartenet","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/RafaelCartenet/mcp-databricks-server/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}