product_scout

io.github.khan-ashifur/hooklayer · Verify this server

Find and rank current TikTok Shop / social-commerce product opportunities using evidence rather than opinion. Use it to answer questions like 'what skincare products are breaking out in the US under $40', 'show me products with strong reviews but low competition', or 'what is worth testing in this niche right now'. Returns products ranked by a deterministic 9-component engine (demand, momentum, creator_adoption, competition, saturation, review_strength, price_attractiveness, creator_concentration, freshness), each carrying an opportunity status (emerging | promising | crowded | mature | cooling | insufficient_data), a confidence band, evidence-cited why_now[], risks[], insufficient_signals[] and history_coverage. The ranking is deterministic and contains no model output - read why_now, risks and components to explain to the user WHY something ranked where it did, and never present the score on its own. Provider-reported fields live under provider_reported.* and are null when TikTok did not expose them; null means unknown, never zero, and sold counts are never multiplied by price to imply revenue. There is no revenue or GMV field. To validate a single product in depth once you have a shortlist, call analyze_product - product_scout ranks a field of candidates, analyze_product interrogates one. Not for generic video search (use search_videos), creator analysis (use analyze_account), or any guaranteed-sales claim. Cost: 5 credits. Requires the shop_intelligence entitlement.