Databricks Genie MCP Server

The Databricks Genie MCP Server bridges the gap between large language models and enterprise data lakes by exposing the Genie conversational AI capabilities as a first‑class MCP resource. In practice, it allows an LLM such as Claude to ask natural language questions that are automatically translated into SQL or other Databricks‑specific queries, retrieve results, and continue a dialogue across multiple turns. This removes the need for developers to write custom adapters or build their own query generators, giving them instant access to powerful analytics pipelines directly from the assistant’s chat interface.

At its core, the server offers a set of lightweight tools that map to Genie’s API endpoints. Developers can list available Genie spaces, fetch descriptive metadata for a chosen space, initiate a new conversation with a question, and issue follow‑up queries within the same context. Each tool returns structured data—SQL statements, result tables, and conversation identifiers—that the LLM can use to refine subsequent prompts or present insights in a human‑readable format. Because the server operates over stdio, it integrates seamlessly with any MCP‑compliant client, whether that’s Claude Desktop, a custom web UI, or an orchestrated workflow in a CI/CD pipeline.

Key features include:

• Space discovery and metadata retrieval – Quickly enumerate the Genie spaces you have access to and understand their purpose without digging through Databricks UI.
• Natural language to SQL conversion – Turn a simple question like “What are the top 10 sales by region?” into an executable query, and receive a tabular result.
• Conversation continuity – Maintain context across multiple turns by passing the conversation ID, enabling the assistant to ask clarifying questions or drill deeper into a topic.
• Structured responses – The server returns results in JSON‑compatible formats, making it trivial to embed tables directly into markdown or pass them to downstream services.

Real‑world scenarios that benefit from this server include data analysts who want instant, conversational access to fresh insights without writing SQL, product managers who need quick exploratory queries during stakeholder meetings, and data engineers who wish to prototype analytics workflows in a conversational sandbox. By exposing Genie’s capabilities through MCP, teams can rapidly iterate on data‑driven prompts, embed analytics into chatbots, or automate reporting pipelines without maintaining complex integrations.

Unlike generic SQL adapters, the Databricks Genie MCP Server leverages Genie’s own conversational intelligence, meaning it can handle ambiguous queries, suggest alternatives, and manage context over long dialogues. This unique advantage reduces friction for non‑technical users while still giving power users the ability to drill down into raw query results. The server’s minimal dependencies, clear resource definitions, and tight security controls (e.g., token scoping) make it a practical addition to any AI‑centric data stack.