The agent assembled a partial but substantive explanation of mpep.io by combining direct site fetches with web search context. It successfully identified what the site does (structured MPEP data for AI integration), who it's for (patent attorneys and developers), and key differentiators (machine-readable citation graphs). However, it could not find pricing information or business model details, and lacked comprehensive product positioning—these gaps were forced, not addressed by the site's architecture.

• ›Steps [1], [2], [3], [24], [30] returned 200 responses for the homepage and section pages (/authorities, /agents, /section/2104, /section/101), but the HTML was heavily truncated and unreadable—the agent could not extract text content from the fetches themselves. All substantive product claims came from web search result snippets [16], [18], which surfaced a single search result linking to '/case/in-re-antonie' with description 'mpep.io — the MPEP as structured data.'
• ›The site does not publish an about page, pricing page, API documentation, or business model statement. Steps [4], [8], [9], [10] all returned 404s (/pricing, /about, /docs, /api). The agent had to rely on web search to piece together what mpep.io is, rather than discovering product definition on-site.
• ›The site is poorly discoverable via web search (confirmed by step [16] returning only one direct result about mpep.io itself, and subsequent searches [5], [6], [12], [13], [19], [20] returning mostly USPTO or competing product results). This forced the agent to make educated guesses about functionality based on URL patterns (/authorities, /agents, /case/, /section/) rather than explicit documentation.
• ›The agent explicitly noted three critical gaps: no pricing/business model, no founder/team information, and no detailed API documentation. These are structural holes in the site's agent-readiness—a machine or AI reading the site has no programmatic way to understand cost, access tier, or integration method.