La Luer — AI Skincare Commerce

search_products

Read-only

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.

Input Schema

TableJSON Schema

Name	Required	Description
`query`	Yes	Search query (e.g. 'vitamin c serum', 'anti-aging', 'moisturizer under $50')
`category`	No	Filter by exact product category from the catalog (e.g. 'serum', 'treatment', 'cleanser', 'moisturizer'). Do not guess categories — only use this if the user explicitly mentions a catalog category. For general queries like 'devices' or 'bundles', use the query parameter instead.
`max_price`	No	Filter to products at or below this price
`max_results`	No	Maximum products to return (default 10). Only set this if the user specifies a count — e.g. 'show me 2 devices' → 2. Otherwise leave it unset and the default will return all relevant matches up to 10.

TDQS

A4.4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds behavioral specificity by stating it returns all matching products with prices, images, and checkout, and clarifies it does not score/filter unlike skincare_recommend, providing context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with purpose, use cases, and explicit alternative. Every sentence contributes meaning without redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Description covers return format, use cases, and alternative tools; schema covers all parameter details. Lacks explicit handling of edge cases like zero results or pagination, but max_results parameter and overall simplicity make it adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% description coverage with detailed explanations for all four parameters (e.g., category warns against guessing, max_results explains default). The description adds no new parameter-specific semantics beyond mentioning 'browse by category', so performance is at baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description states 'Browse and search the product catalog' with specific verbs and resource, and explicitly contrasts itself with skincare_recommend, distinguishing it from sibling tools. It also lists concrete use cases like 'show me' and 'what do you have'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use scenarios and an explicit exclusion: 'Do not use when the user needs personalized recommendations... use skincare_recommend instead.' This clearly guides selection against the most similar sibling tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.1/5.0

Disambiguation4/5

Most tools have distinct purposes, but there is some overlap between skincare_cart and create_checkout, as both handle checkout creation, which could cause confusion. Additionally, search_products and skincare_recommend are well-differentiated by their descriptions, but an agent might misselect between them if the user's intent is ambiguous. Overall, the tools are mostly clear with minor areas of potential overlap.

Naming Consistency3/5

The naming follows a mixed pattern: some tools use verb_noun (e.g., check_compatibility, compare_products), while others use noun_verb (e.g., skincare_recommend, skincare_cart). This inconsistency, with variations like deals_discounts (plural nouns) and skincare_report_issue (noun_noun), reduces predictability. However, the names are still readable and descriptive, avoiding chaotic conventions.

Tool Count5/5

With 10 tools, the count is well-suited for an AI skincare commerce server, covering key e-commerce and recommendation functions without being overwhelming. Each tool serves a specific role in product discovery, inventory, comparison, checkout, and support, making the set appropriately scoped for the domain.

Completeness4/5

The toolset provides comprehensive coverage for skincare commerce, including product search, recommendations, inventory checks, compatibility analysis, and checkout processes. Minor gaps exist, such as the lack of tools for updating or managing user accounts or handling post-purchase support like returns, but core workflows are well-covered, allowing agents to function effectively.

Resources

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