Check if a product is currently available. Uses Shopify Storefront API to verify real-time stock status. Use when a customer asks 'is MIRA in stock?' or before recommending a product.
| Name | Required | Description | Default |
|---|---|---|---|
| product | Yes | Product name or SKU to check availability for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds valuable context beyond annotations by noting the tool uses the Shopify Storefront API and checks 'real-time stock status,' implying freshness of data. It does not describe return format or edge cases, but for a read-only check, the annotations plus real-time detail are sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main action ('Check if a product is currently available'), and includes a concrete example customer query. Every sentence earns its place: purpose, implementation detail, and usage guidance without filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a simple tool with one well-documented parameter, strong annotations, and no output schema. The description covers the core purpose and real-time nature, and the phrase 'check if available' implicitly signals a boolean-like result. Missing details like behavior for unknown products are minor, so the description is sufficiently complete for this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%: the single parameter 'product' is described as 'Product name or SKU to check availability for.' The description adds no additional meaning beyond the schema (it only restates 'product'), so the baseline 3 applies without any enhancement.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb and resource: 'Check if a product is currently available.' It further narrows scope with 'real-time stock status' and includes a concrete use case ('is MIRA in stock?'). This distinguishes it from sibling tools like get_product or search_products, which focus on product details or search rather than availability.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit triggers: 'Use when a customer asks ... or before recommending a product.' This gives clear context for when to invoke the tool. However, it does not mention when not to use it or name alternative tools, so it falls short of an explicit when/when-not with alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.