The agent successfully gathered comprehensive information about MCRender's core offering, pricing, target audience, and positioning. The site is moderately navigable but heavily client-side rendered, forcing the agent to rely on web search results and prior knowledge to extract detailed feature information that wasn't readily accessible from the fetched HTML. The agent assembled a coherent explanation despite incomplete data extraction from the pages themselves.

• ›Steps [1, 2, 3] and [6, 7] fetched the homepage, pricing, and about pages, but the HTML responses were truncated and Next.js-rendered, containing minimal extractable text content. The agent could not directly parse pricing tiers or feature details from these fetches.
• ›The agent relied on web search results ([5], [8], [9], [10], [11], [12], [13]) to fill in the gaps—search snippets contained pricing information, feature lists, and positioning details that were not accessible from the raw page fetches. For example, pricing plans ($5/month Hobby, $15/month Creator, $199 lifetime) and the 30,000+ creator user count came from search results, not direct page content.
• ›Step [9] search results explicitly surfaced the 'AI Pose Studio' feature page URL (/studio/ai), which the agent cited in its sources but could not actually fetch and parse successfully, leading to acknowledged uncertainty about AI capabilities.
• ›The site does not publish structured pricing data (e.g., JSON-LD, meta tags, or a plain text pricing table) in a machine-readable format; all pricing information had to be inferred from search engine snippets or reconstructed from partial page captures.
• ›The agent was transparent about gaps: it noted inability to clarify exact AI capabilities, full pose library scope, and commercial licensing terms—all limitations arising from the Next.js client-side rendering making detailed content inaccessible to the fetcher.