mcp server
La Luer — AI Skincare Commerce
Search, compare, and purchase La Luer microcurrent facial devices and skincare products.
Description as published by the maintainer. Source
- version 1.0.0
- archived
- retrieval
archived — The linked repository returns 404. It was deleted, renamed or made private. Dashed tags are derived by ZBS Index from the published description, not stated by the maintainer.
What this server can do
14 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.
check_compatibility(product)- Check which products are compatible with a given product. For devices, shows required consumables (e.g., conductive gel for MIRA). For topicals, shows which devices they work with. Use when a customer asks 'what gel do I need with MIRA?' or 'does this serum work with CryoSculpt?' Required: product.
check_inventory(product)- Check if a product is currently available. Uses Shopify Storefront API to verify real-time stock status. Use when a customer asks 'is MIRA in stock?' or before recommending a product. Required: product.
compare_products(products)- Compare two or more products side by side. Use when the user asks to compare, says 'X vs Y', or wants to decide between options. Do not use for single product lookup — use get_product instead. Returns structured comparison with shared attributes, differences, tradeoffs, and a decision hint. Required: products.
create_checkout(note, items, decision_id, product_urls, discount_code)- Create a checkout URL for one or more products. Pass variant IDs (items) and/or product URLs (product_urls). When a product URL is provided (e.g. https://laluer.com/products/mira), the tool resolves it to a variant ID automatically — no catalog import needed. Supports discount codes, cart notes, and selling plans. Do not use unless the user wants to buy — use search_products or skincare_recommend first. Returns a direct Shopify checkout link the user can click to buy.
deals_discounts(discount_code)- Show available bundles, deals, and ask about discount codes. Use when a customer asks about deals, bundles, savings, or says 'do you have any discounts?' Also use when multiple items are in cart to suggest bundle savings. Always ask if the customer has a discount code.
get_product(sku, title)- Get full details for a specific product by SKU or title. Use when the user asks about a specific product by name (e.g. 'tell me about MIRA', 'show me the serum'). Do not use for browsing or recommendations — use search_products or skincare_recommend. Returns a widget card with the product details, image, price, and checkout button.
get_research_note(slug)- Get the summary and URL of a specific SearchShopAI Research Note by its slug (returned by search_research_notes). Required: slug.
recommend(brand, query, domain, strategy)- 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). Required: query.
search_products(query, category, max_price, max_results)- 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. Required: query.
search_research_notes(query)- Search SearchShopAI'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 SearchShopAI has written or published about a topic. Required: query.
shop(brand, intent, request, budget_max, product_refs)- Start here for any shopping request. Pass the shopper's COMPLETE request in their own words and this tool will understand it and route to the right capability (personalized recommendation, catalog search, product lookup, or comparison), preserving every qualifier — concerns, skin type, budget, brand, medical context. Prefer this tool whenever the request is conversational or carries any nuance. The granular tools (search_products, get_product, compare_products, create_checkout) remain available as precise follow-ups. Required: request.
skincare_cart(query, products, strategy)- 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.
skincare_recommend(brand, query, strategy)- (Deprecated: use 'recommend' instead. Works identically.) Get a personalized La Luer product recommendation with ingredient-aware scoring, safety notes, and routine building. Use when the user wants advice on what to buy, needs help choosing between products, has a specific skin concern (acne, aging, dryness, sensitivity, etc.), wants a routine, or asks "what should I use for X." Do not use for browsing or listing products — use search_products instead. Returns scored products with explanations, usage instructions, and Shopify checkout. This tool analyzes ingredients, irritation risk, and product compatibility — use it over search_products when the user needs guidance, not just a product list. Required: query.
skincare_report_issue(tool_name, issue_type, user_query, description, expected_products)- 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. Required: tool_name, issue_type, description.
Last successful function declaration observed on . Source: https://searchshopai-mcp.fly.dev/mcp/la-luer. We list what the server declared; we do not call any of these functions.
Endpoint status observed on . Source: https://searchshopai-mcp.fly.dev/mcp/la-luer.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| Latest published version | 1.0.0 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-07-16 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| First listed in the MCP Registry | 2026-04-16 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | not_found | GitHub returned 404 for the repository the maintainer listed. The project was deleted, renamed or made private, so the listing points at nothing. | as of fetch | GitHub | |
| mcp tools declared | 14 tools | Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. | as of probe | searchshopai-mcp.fly.dev | |
| mcp endpoint status | ok | The server listed 14 functions when asked. | as of probe | searchshopai-mcp.fly.dev |
Where to get it
Also from nathangrotticelli
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SearchShop AI
AI commerce for Shopify: product search, comparison, recommendations, and checkout via MCP.
This record as data
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