La Luer — AI Skincare Commerce

skincare_cart

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.

Input Schema

TableJSON Schema

Name	Required	Description
`query`	No	Natural language query to auto-recommend products. Only used if products array is not provided.
`products`	No	Specific product titles to add to the cart
`strategy`	No	Optional offer strategy override when using query mode

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already indicate this is a write operation (readOnlyHint=false) and not destructive (destructiveHint=false). The description adds that it returns a widget with cart items and a working checkout link, clarifying the output and emphasizing 'real' checkout behavior. It doesn't contradict annotations but could provide more detail on side effects like cart persistence.

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 three sentences: purpose, modes, and usage boundary. Every sentence adds distinct information with no fluff, making it easy to scan.

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?

For a tool with three optional parameters and no output schema, the description adequately covers the return format (widget with cart items and checkout URL) and when to use it. It doesn't mention the optional 'strategy' parameter, but the schema covers that, and the description's focus on the two primary modes is sufficient for an agent to invoke correctly.

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% since every parameter has a description, but the description adds value by explaining the relationship between the 'products' array and 'query' string: products takes precedence, and query is used only if products is not provided. This conditional logic goes beyond the schema's individual definitions.

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 opens with a specific verb+resource: 'Create a buyable shopping cart with a real checkout URL.' It clearly differentiates from browsing/recommendation tools by explicitly telling the agent to use search_products or skincare_recommend first, establishing its unique role.

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?

It includes an explicit exclusion: 'Do not use for browsing or recommendations — use search_products or skincare_recommend first.' It also describes two usage modes with clear conditions: pass a 'products' array for specific items or a 'query' string for auto-recommendations, giving the agent clear selection criteria.

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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