Why general-purpose AI integrations fall short — and how building your own MCP server changes the game
7 min read
Just now
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We had Claude available in our workflow.
We connected it to the codebase, pointed it at our docs, and waited for the productivity boost everyone was talking about.
It helped. But it kept answering as if our project didn’t exist.
It didn’t know what a fulfillment_hold meant in our order pipeline.
It suggested column names that didn't match our schema.
It recommended patterns that violated the conventions we'd spent months aligning on.
The AI was smart — just not our smart.
The missing piece wasn’t a better model.
It was context.
And the best way to give an AI model real, structured access to your team’s context is through a Model Context Protocol (MCP) server — specifically, one you build yourself.
Off-the-shelf MCP tools are great for getting started. But they’re built for everyone, which means they’re optimized for no one.