Databricks MCP Server

Overview

The Databricks MCP Server is a specialized bridge between AI assistants and the Databricks Unity Catalog (UC). It turns rich, structured metadata—catalogs, schemas, tables, columns, and their descriptive annotations—into actionable knowledge that an LLM can consume in real time. By exposing UC metadata through the Model Context Protocol, the server lets an AI agent discover what data exists and why it matters, enabling the generation of precise SQL queries without human guidance.

At its core, the server offers a suite of tools that empower an agent to explore data lineage beyond simple table references. It can traverse notebook and job dependencies, retrieve the actual code that reads from or writes to tables, and surface business rules or data‑quality checks embedded in those artifacts. This deep lineage insight allows an agent to reason about the provenance of data, anticipate downstream effects of changes, and validate that transformations align with business intent—all within a single conversational loop.

Developers benefit from this server in several concrete ways. First, the agent can autonomously answer data‑centric questions: “Which tables contain customer churn metrics?” or “What columns are used in the revenue calculation pipeline?” Second, by understanding table relationships and lineage, the agent can propose or refine SQL queries that respect schema constraints and business logic. Third, when a query fails or returns unexpected results, the agent can drill into the originating notebooks to identify potential logic errors. This iterative feedback cycle reduces the need for manual debugging and accelerates data exploration.

Typical use cases include self‑service analytics portals where end users ask natural‑language questions that are translated into SQL, automated data quality monitoring systems that flag lineage violations, and continuous integration pipelines that verify transformation logic against updated schemas. In all scenarios, the MCP server removes the friction of manual catalog browsing and code inspection, letting developers focus on higher‑level business questions while the AI handles the heavy lifting of data discovery and query generation.