The agent successfully assembled a comprehensive explanation of Namefi by combining fetches from the homepage, API documentation files (llms.txt and llms-full.txt), and blog posts. The site's information was scattered across multiple formats and required web search to surface comparison content; pricing was entirely absent from public-facing pages, forcing the agent to acknowledge a gap rather than provide a complete answer.

• ›The llms.txt and llms-full.txt files ([7], [8]) were the most information-dense sources, providing core product definition ('register traditional domains as NFTs and manage their DNS records via API'), target audience ('agent-native'), and technical architecture (MCP server). These files appear designed for AI agent consumption but are not discoverable from the homepage alone.
• ›The agent had to use web search to find the blog posts ([12], [13]) that explained differentiation vs. Cloudflare and Name.com. The homepage and standard navigation (/pricing, /about) returned 404s, indicating a SPA-heavy site where marketing content is not in conventional locations or requires JavaScript rendering.
• ›Pricing was entirely missing from all fetched pages—the agent correctly noted this gap and cited marketing language ('cheaper', 'easier') without actual numbers. The site does not publish a pricing page in any discoverable format, making this dimension of the task genuinely unanswerable from the website alone.
• ›The site's agent-readiness is mixed: it provides structured documentation (llms.txt, llms-full.txt) and MCP server discovery, but traditional navigation and information architecture are weak. The agent had to rely on 63% prior artifact context and 25% web search, suggesting Namefi's own site structure does not easily yield the full picture.