breakdown.live
success
Claude Code
intent

What does breakdown.live do and who is it for? Explain it back to me.

5steps
37.3sduration
$0.0562cost
29,684tokens
9 steps4 reasoning steps1 search
home
docs
/how-it-works
/faq
search
100%
on-site discovery
100%
reliability
75%
link following
path origin
  • previous resource75%
  • prior knowledge25%
insight

The agent successfully gathered comprehensive information about Breakdown.live from the site's primary pages and synthesized a clear, well-structured explanation of what the product does, who it serves, how it's priced, and how it differs from alternatives. The site was well-navigable, with clear information architecture (homepage, pricing, how-it-works, FAQ) that contained all necessary details to answer the task; the agent did not need to rely on web search results for substantive content.

  • ›Step [1] (homepage) provided the core value proposition via meta descriptions and page content: a Mac network-monitoring app that diagnoses whether connection problems originate in Wi-Fi, ISP path, or app/service layers.
  • ›Step [3] (pricing page) explicitly surfaced the freemium model with specific pricing ($29/month or $25/month annual) and feature differentiation between Free and Pro tiers, making the pricing structure directly machine-readable.
  • ›Step [4] (how-it-works page) and Step [5] (FAQ) contained the methodology and technical details (DNS checks, TCP probes, topology diagrams, privacy/permissions model) that enabled the agent to articulate what makes Breakdown different from generic bandwidth-monitoring tools.
  • ›The agent correctly identified that the site does NOT publish competitor comparisons or multi-platform availability—gaps the agent explicitly called out in 'Not mentioned on their site' section rather than hallucinating answers.
  • ›Site architecture was discovery-friendly: all four fetched pages were directly navigable from the homepage or clearly linked siblings, indicating the information was deliberately organized for user/agent accessibility.
  • ›The agent used 75% prior knowledge + 25% direct guessing (prior knowledge for the initial fetch), suggesting the homepage structure and domain name were guessable, but all substantive content came from explicit page fetches—no reconstruction from fragments was needed.

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