MCP Dev Summit North America

AI agents are rapidly becoming the primary consumers of technical documentation. At Apollo GraphQL, our traffic data shows agents on pace to become our #1 traffic source, prompting a shift in how we write, structure, and serve docs. We now have two distinct audiences that read differently: AI needs patterns, not paragraphs. Connecting sentences that help humans are now wasted tokens.

As the sole documentation engineer at Apollo, I’ve spent the past year building MCP tooling for our docs. This talk covers what worked, what failed, how we tested, and what surprised us.

I’ll walk through the evolution from serving AI full pages to chunked retrieval strategies that balance completeness with token usage. You’ll see how we used AI tooling to restructure docs for AI readability and exposed them as MCP tools agents are compelled to use.

To measure progress, we built an evaluation suite: a sandboxed runner that executes end-user prompts against our MCP server, builds an application, seals the output, then passes it to a second model for scoring against a reference solution and rubric. I’ll demo this live and share how it produced stable metrics in a non-deterministic AI world.