Databricks Genie MCP Server

Databricks Genie API MCP Server

The Databricks Genie API MCP server bridges the gap between enterprise data platforms and conversational AI assistants. It exposes the full breadth of Databricks Genie’s no‑code, natural‑language data exploration capabilities as a set of MCP tools. Developers can therefore embed Genie’s powerful question‑answering and SQL generation directly into any MCP‑compatible client—Claude Desktop, for example—without writing custom connectors or SDK wrappers. This eliminates the friction of building a bespoke conversational layer on top of Databricks, enabling rapid prototyping and production‑ready data analytics workflows.

At its core, the server provides a standardized interface for three key activities: (1) initiating and managing Genie conversations; (2) creating and retrieving conversational messages; and (3) executing the SQL queries that Genie produces and returning their results. Each operation is represented as an MCP tool with clear input schemas, allowing an AI assistant to orchestrate complex data queries in a single prompt. Because the server authenticates directly against Databricks using PAT or OAuth service principals, all data access follows existing workspace security policies and can be scoped to specific Unity Catalog permissions or SQL warehouses.

The value proposition for developers is multifold. First, it removes the need to handle Databricks authentication and session management manually; the MCP server handles token renewal and secure communication. Second, it standardizes the toolset so that any AI client can invoke Genie’s natural‑language interface without custom code, fostering interoperability across organizations. Third, the server exposes SQL execution results in a machine‑readable format, enabling downstream automation or visualisation tools to consume the data directly from the assistant’s output.

Typical use cases include business intelligence dashboards that allow end users to ask ad‑hoc questions in plain English, automated compliance reporting where a conversational agent fetches audit logs or PII access metrics, and data science pipelines that trigger SQL jobs based on natural‑language prompts. In each scenario, the MCP server acts as a lightweight, secure gateway that translates conversational intent into precise Databricks operations, preserving the integrity of data governance while delivering instant insights.

What sets this MCP server apart is its tight integration with Genie’s conversational engine. Unlike generic JDBC or REST connectors, the server leverages Genie’s own dialogue management to maintain context across turns, ensuring that follow‑up queries build on previous results. This contextual continuity is essential for complex analytical conversations, where each question may depend on earlier clarifications or data refinements. By exposing these capabilities through MCP, developers gain a powerful, reusable component that can be dropped into any AI workflow—whether in a chatbot, a data‑analysis platform, or an automated monitoring system.