plantgrail.com
success
Claude Code
intent
What does plantgrail.com do and who is it for? Explain it back to me.
17steps
83.5sduration
$1.1031cost
299,982tokens
21 steps4 reasoning steps7 searches
home
docs
docs
docs
search
search
/guide
/help
search
search
search
home
/plants
/courses
search
/shop
search
100%
on-site discovery
70%
reliability
50%
link following
path origin
- previous resource50%
- prior knowledge50%
insight
The agent successfully assembled a comprehensive overview of Plant Grail's purpose, audience, offerings, and differentiation by piecing together information from the homepage, about page, plant directory, courses page, and shop page. The site was reasonably navigable for discovery but lacked transparency on pricing details and free vs. premium access boundaries, forcing the agent to fill gaps with caveated language and acknowledge what could not be retrieved.
- ›Steps [1], [12], [13], [15], and [17] were all directly cited in the final response's source list and contained the core narrative: homepage meta-descriptions and titles revealed the 20,000+ cases and 2,703 solutions; the courses page disclosed 'Mastery Packs from $37'; the shop page confirmed curated product selection; the about page promised methodology details but the agent could not extract full content from the compressed HTML.
- ›The agent successfully guessed /plants, /courses, and /shop URLs without prior knowledge or search hints (they returned 200), indicating a predictable, RESTful site structure. However, /pricing returned 404, forcing the agent to infer pricing from course and shop pages rather than a dedicated pricing document—a common friction point for LLM evaluation.
- ›The site publishes key differentiators (evidence-based, 20,000+ cases, data-driven) prominently in meta tags and page titles, making them machine-readable; however, specific pricing per Mastery Pack, the distinction between free and gated diagnostic content, founder/team identity, and the data collection methodology were not accessible in the fetched HTML. The agent correctly flagged these gaps rather than inventing answers.
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