La Luer — AI Skincare Commerce

recommend

Read-only

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).

Input Schema

TableJSON Schema

Name	Required	Description
`brand`	No	Filter to a specific brand
`query`	Yes	Natural language query about product needs
`domain`	No	Product domain (e.g. 'skincare', 'beauty_devices'). Auto-detected from merchant if omitted.
`strategy`	No	Optional offer strategy override

TDQS

A4/5.0

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 valuable behavioral context by describing the return payload: scored products with checkout URLs, safety assessment, and authority state (SHOULD/CAN/SHOULDNT/ESCALATE/CANT). This goes beyond the annotations and informs the agent about its transactional authority level, which is crucial for deciding next steps.

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?

The description is two sentences, front-loaded with the core purpose and ending with usage guidance and return format. Every sentence delivers distinct value without redundancy or unnecessary detail.

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?

Given moderate complexity and no output schema, the description adequately explains return values (scored products, checkout URLs, safety assessment, authority state). It covers the main user-facing outcomes. It does not mention error cases or behavior for invalid inputs, but with full schema coverage and clear return format, it is sufficiently complete for an AI agent to invoke the tool correctly.

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%, so the baseline is 3. The description does not add specific parameter semantics beyond what the schema already provides (e.g., query is a natural language description, brand filters, domain auto-detection). It mentions domain-expert scoring but does not elaborate on parameter usage, so the schema remains the primary source of parameter meaning.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Get a personalized product recommendation' with domain-expert scoring and safety notes. It also specifies the return types (scored products, checkout URLs, safety assessment, authority state). However, it does not explicitly distinguish itself from sibling tools like 'skincare_recommend', which appears to be a specialized variant, so it lacks sibling differentiation.

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

Usage Guidelines4/5

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

The description provides explicit when-to-use guidance: 'Use when the user wants advice, has a concern, or asks what to buy.' It does not mention alternatives or exclusions, but the context is clear and sufficient for typical recommendation queries.

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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