When Anthropic released the Model Context Protocol (MCP) spec, most developers immediately saw database connectors, file-system tools, and API wrappers. I saw something different: a direct bridge between an AI assistant and the revenue chaos that kills most SMBs. I've spent 15 years fixing broken go-to-market operations — seven major enterprise transformations, including work that contributed to $800M in revenue at Québecor's retail network. Every single engagement had the same root problem: the data existed, but no one could turn it into a decision fast enough. MCP changes that equation. So I built the Artefact MCP Server — the first MCP server purpose-built for GTM revenue intelligence — and published it as open source. Here's what I learned building it, and why the architecture decisions matter if you're thinking about AI-native revenue operations. Why GTM Data Is Uniquely Hard for AI Before I explain what I built, it's worth explaining why this problem is harder than it looks.…