La Luer — AI Skincare Commerce

skincare_recommend

Read-only

(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.

Input Schema

TableJSON Schema

Name	Required	Description
`brand`	No	Filter to a specific brand only (e.g. 'Glossier', 'CeraVe', 'The Ordinary'). Use when the user asks for products from a specific brand.
`query`	Yes	Natural language query about skin concerns (e.g. 'I have oily acne-prone skin and want something gentle under $30')
`strategy`	No	Optional offer strategy override: starter, gentle, budget, glow_safe, minimal, strong, fallback

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. The description adds behavioral context: it analyzes ingredients, irritation risk, and compatibility, and returns scored products with explanations, usage instructions, and Shopify checkout. This goes beyond the annotations without contradiction, though it does not detail every output edge case.

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

Conciseness4/5

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

The description is informative but slightly verbose, containing five sentences. It is front-loaded with the deprecation warning, and every sentence provides distinct value (purpose, usage, exclusions, return format, differentiation). A more concise version could tighten it, but the structure is logical and easy to scan.

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

Completeness5/5

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

No output schema exists, but the description specifies return contents (scored products, explanations, usage instructions, Shopify checkout). It also covers use cases, limitations, deprecation status, and comparison to siblings, making it complete for a recommendation tool of this complexity.

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?

Schema description coverage is 100% for all three parameters (brand, query, strategy), so the schema already carries parameter meaning. The description does not add additional parameter-specific detail; it focuses on higher-level behavior, so the baseline score of 3 is appropriate.

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?

The description clearly states it provides personalized product recommendations with ingredient-aware scoring, safety notes, and routine building. It differentiates from siblings by explicitly deprecating in favor of 'recommend' and contrasting with search_products for browsing/listing.

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?

The description gives explicit when-to-use scenarios (advice, choosing between products, specific skin concerns, routine building, 'what should I use for X') and when-not-to-use (browsing/listing, directing to search_products). It also explicitly prefers this tool over search_products for guidance, covering both inclusion and exclusion.

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.

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