Implementing MCP in the Lakehouse

This article provides a comprehensive guide on implementing the Model Context Protocol (MCP) in a lakehouse environment. It explains how MCP, introduced by Anthropic, standardizes AI agent interaction with data through tools, resources, and prompts. The content covers the MCP specification, a reference architecture for lakehouse MCP servers, building an MCP server in Python using the official SDK, authentication patterns, deployment, testing, and common failure modes. The reference implementation is Dremio’s open-source MCP server, demonstrating how to expose Iceberg tables and query engines as agent-accessible tools.