The agent successfully understood and explained get-split.app's core value proposition, target audience, pricing, and differentiation from competitors by assembling information from the homepage, blog posts, and a direct comparison article. However, the site's Hebrew-only language and truncated HTML responses limited full access to detailed features and pricing documentation, forcing the agent to work from partial content and infer key details.

• ›Step [1] (homepage) and step [6] (how-it-works page) returned the core product description in Hebrew, requiring the agent to decode the value proposition from untranslated content. These were the primary sources for understanding what the product does and who it's for.
• ›Steps [21] and [22] discovered blog articles directly comparing Split to Splitwise and explaining restaurant bill-splitting workflows. The agent cited these as sources for differentiation claims, but the HTML was truncated ('4400 more chars') — the agent constructed its final comparison answer from partial content and page titles alone.
• ›The site offers no English version, dedicated pricing page, or features documentation. The agent could not verify pricing claims (though it inferred 'free' from context), could not find information about usage limits or premium tiers, and had to guess at feature completeness.
• ›Steps [7] and [8] (attempts to fetch /pricing and /features) returned 404s, indicating either missing pages or a limited site structure. The agent adapted by relying on blog content and the homepage, showing reasonable fallback behavior but exposing poor information architecture for answering structured questions.
• ›Web searches [3], [12], [13], [15], [16], [18] were routers that did not directly contribute content — they failed to surface get-split.app details and instead returned competitors (Splitwise, Tab, Splid). The agent ultimately relied on direct URL guessing and prior knowledge rather than search-powered discovery.