La Luer — AI Skincare Commerce

shop

Read-only

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.

Input Schema

TableJSON Schema

Name	Required	Description
`brand`	No	Secondary hint only — brand filter if stated.
`intent`	No	Secondary hint only — the primary signal is `request`.
`request`	Yes	The shopper's complete request in their own words. Include every detail: concern, skin type, budget, brand, product names, questions, medical context. Do not summarize or drop qualifiers.
`budget_max`	No	Secondary hint only — maximum budget if stated.
`product_refs`	No	Secondary hint only — exact product titles/SKUs when already known.

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds transparency about its routing/natural-language-understanding behavior and emphasizes lossless preservation of qualifiers. It does not mention return format, but this is less critical given the safety annotations and no output schema.

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 four sentences, front-loaded with 'Start here', and every sentence provides actionable value. It avoids redundancy and is appropriately sized for a router tool's complexity.

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 the tool has one required parameter and no output schema, the description adequately covers input requirements, capabilities, and alternative tools. It does not enumerate all possible intents (e.g., 'buy', 'question') but the 'any shopping request' phrasing is inclusive. Slight gap on return behavior is acceptable without an output schema.

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

Parameters4/5

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

Schema coverage is 100% with descriptions for each parameter, so baseline is 3. The description adds significant meaning by stressing that `request` is the primary signal and all other parameters are 'secondary hints only', which is partially in the schema but reinforced through the instruction to pass the complete request verbatim. This clarifies how to prioritize inputs.

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 the tool's role: 'Start here for any shopping request' and explains it routes to capabilities like personalized recommendation, catalog search, product lookup, or comparison. It distinguishes itself from the granular sibling tools by naming them explicitly as precise follow-ups.

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 guidance: 'Prefer this tool whenever the request is conversational or carries any nuance.' It also states that granular tools (search_products, get_product, compare_products, create_checkout) remain available for precise follow-ups, providing clear exclusions and alternatives.

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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