Agent successfully retrieved comprehensive information by fetching the homepage, FAQ, about page, and producer guide, assembling a coherent explanation of Self-sown's positioning, pricing, and decentralized model. The site content was well-organized and directly navigable, though some messaging inconsistencies and operational details remained unexplained in the published materials.

• ›All five fetched pages were content-bearing and contributed specific facts: homepage established the core value proposition; FAQ clarified permissionless mechanics and encryption; about page articulated mission and business model; producer guide revealed the three pricing tiers with concrete numbers ($0, $21/mo, $2,100 lifetime). Site structure supported linear discovery.
• ›Pricing information was split across multiple pages (homepage implied 'no fees,' producer guide disclosed optional memberships). The agent correctly synthesized this but flagged a genuine messaging gap: the site emphasizes 'no fees' without initially clarifying that the Free tier exists alongside paid tiers, creating ambiguity about whether fees are truly optional or mandatory.
• ›The site explicitly states sellers avoid transaction fees by connecting their own Stripe/Square accounts, but does not spell out what percentage those third-party processors charge. Agent recognized this gap and called it out as incomplete cost transparency, rather than inventing figures—strong grounding in what was actually present.
• ›Homepage and FAQ were typed from memory (not discovered via site navigation), suggesting the site lacks a clear internal navigation structure or sitemap. Agent had to infer that /faq and /producer-guide existed rather than being led there. Only /about and /producer-guide were discovered via follow links.
• ›Agent correctly identified unexplained features ('Model Context Protocol' for AI agents, 'NIP-17' encryption standard) and flagged missing operational docs (shipping, disputes, order workflow), demonstrating discernment about what the site publishes vs. what users need to know.