La Luer — AI Skincare Commerce

Get the summary and URL of a specific SearchShop AI Research Note by its slug (returned by search_research_notes).

Get a personalized product recommendation with domain-expert scoring, safety notes, and transaction authority. Use when the user wants advice, has a concern, or asks what to buy. Returns scored products with checkout URLs, safety assessment, and authority state (SHOULD/CAN/SHOULDNT/ESCALATE/CANT).

Browse and search the product catalog. Use when the user wants to see what's available, look up specific products, browse by category, compare options, or asks 'show me' / 'what do you have.' Do not use when the user needs personalized recommendations based on skin concerns — use skincare_recommend instead. Returns all matching products with prices, images, and checkout. Unlike skincare_recommend, this does not score or filter — it shows everything that matches so the user can decide.

Search SearchShop AI's Research Notes blog — data studies, playbooks, and field notes on agentic commerce (AI attribution, MCP, AI catalog accuracy, ChatGPT ads). Returns matching articles with titles, summaries, and URLs. Use when asked what SearchShop AI has written or published about a topic.

Create a buyable shopping cart with a real checkout URL. Two modes: (1) Pass 'products' array with specific product names. (2) Pass 'query' string to auto-recommend and cart. Do not use for browsing or recommendations — use search_products or skincare_recommend first. Returns a widget with the cart items and a working checkout link.

(Deprecated: use 'recommend' instead. Works identically.) Get a personalized skincare recommendation with ingredient-aware scoring, safety notes, and routine building. Use when the user wants advice, has a skin concern, or asks what to buy. Do not use for browsing or listing products — use search_products instead. Returns scored products with checkout URLs.

Report when a tool result was unhelpful, incomplete, or wrong. Call this whenever you override a recommendation, skip a cart result, or notice the engine output doesn't match what the user needs. Do not use proactively — only when you observe an actual issue. This helps improve the engine.