The agent successfully assembled a comprehensive understanding of Breakout Scanner by relying almost entirely on machine-readable documentation (llms.txt, pricing.md, llms-full.txt, index.md) rather than the rendered website. The site publishes well-structured, LLM-ready artifacts that directly answered all core questions about what the product does, who it's for, pricing, and differentiation—making the discovery efficient despite the homepage and standard navigation pages being inaccessible to the agent's content extraction.

• ›Steps [4], [5], [6], [7] all returned markdown/text documentation files (.txt, .md) that contained authoritative, complete answers: llms.txt provided the 'when to use' and core positioning; pricing.md delivered exact pricing ($20 promo/$30 regular, 7-day trial, single-tier structure); llms-full.txt expanded on features, backtesting methodology, global exchange coverage, and use cases; index.md reiterated positioning and use cases.
• ›The site does NOT publish pricing at /pricing (404 in step [1]), but DOES publish it at /pricing.md—a well-known artifact pattern. The agent discovered this through prior knowledge (88% of fetches sourced from previous artifacts), indicating the site owner has optimized for AI agents by publishing structured docs in parallel with the rendered HTML site.
• ›The agent could not access rendered HTML content (homepage, about, support pages returned HTML that the tool truncated to 4400 chars—too short to extract semantic content), but the availability of llms.txt and llms-full.txt as fallback surfaces meant the agent never got stuck. The site is highly agent-ready in documentation but opaque in rendered form.
• ›The agent correctly identified gaps in what the site exposes: no third-party reviews, no detailed backtest methodology, no free tier details, no performance examples, no customer support channel info. These are discovery/experience friction points that the site does not document—forcing the agent to note them as 'missing' rather than 'not found but retrievable.'
• ›Competitive differentiation was inferred from feature comparison (natural-language screening, integrated backtesting, global 21-exchange coverage, single pricing) rather than explicitly stated on the site. The site does not publish a 'vs. competitors' or 'alternatives' document, requiring the agent to synthesize from product docs and external search results.