get-stacks.app
success
Claude Code
intent

What does get-stacks.app do and who is it for? Explain it back to me.

15steps
95.4sduration
$1.0280cost
147,438tokens
22 steps7 reasoning steps7 searches
home
docs
docs
/features
search
search
/sa/app/stacks-book-sc…
search
docs
docs
search
docs
search
search
search
50%
on-site discovery
75%
reliability
13%
link following
path origin
  • previous resource13%
  • web search50%
  • prior knowledge38%
insight

The agent gathered a reasonably complete overview of Stacks by assembling information from third-party review sites and the App Store, since the official website (get-stacks.app) is a React SPA that does not surface detailed content through static HTML. The agent successfully answered all four aspects of the task—what Stacks does, who it's for, how it's priced, and how it differs from alternatives—but had to rely entirely on external sources and prior knowledge rather than the publisher's own website, indicating poor agent-readiness on the domain itself.

  • ›Steps [1], [3], [4], [5] all fetched get-stacks.app URLs but returned identical minimal HTML (React boilerplate) with only metadata from the meta description tag. The site's content is client-rendered and not accessible via static HTTP responses.
  • ›The pricing model ($4.99/month or $34.99/year for Pro; 3 free scans) and feature list (shelf photography with CV, AI reading concierge, visual spine rendering, lending tracker) came from third-party review aggregators ([13] Plumerie, [14] BookScouter), not from get-stacks.app itself.
  • ›The key differentiator (shelf photography via computer vision vs. barcode scanning) was inferred from third-party comparisons, not explicitly stated on the publisher's website. The agent had to piece together competitive positioning from external sources.
  • ›Platform limitations (iOS-only, no Android info found), accuracy rates, database sources, and detailed FAQs remain unknown because the official site does not publish this information in a fetchable form.
  • ›The agent's prior knowledge (50% web search, 38% prior knowledge, 13% previous artifact) was necessary to assemble the answer; the domain itself provides almost no agent-navigable information.

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