The agent successfully gathered and synthesized a clear overview of Courts' purpose, audience, pricing model, and positioning by fetching the homepage and llms.txt file. The site publishes its core positioning directly on these pages, but lacks dedicated documentation on business model, editorial processes, and data freshness, which the agent transparently flagged.

• ›The homepage and llms.txt both articulated the site's core function (directory, not booking platform) consistently and directly — no assembly required. The agent cited both as sources and drew from their complementary detail levels (homepage more granular on venues; llms.txt more on purpose and audience).
• ›The site provides machine-readable exports (llms.txt, llms-full.txt, sitemap.xml) that the agent discovered via a link from the homepage, signaling agent-ready structure, though llms.txt itself contains no explicit business model or revenue statement.
• ›Data quality issues (102 vs. 106 venue count, HK$0 and HK$2000 outliers, missing district/MTR/price fields) were present in the fetched content and the agent correctly surface-reported them without guessing root cause, showing disciplined interpretation.
• ›The agent did not search externally (web search returned 0% of discovered URLs) and completed the task from on-site content alone, confirming the site's self-sufficiency for this discovery task.
• ›The agent transparently noted what was absent (no About, Pricing, FAQ, Docs pages; no explanation of partnerships or update cadence) rather than inventing plausible answers, demonstrating appropriate epistemic restraint.