Compare two or more products side by side. Use when the user asks to compare, says 'X vs Y', or wants to decide between options. Do not use for single product lookup — use get_product instead. Returns structured comparison with shared attributes, differences, tradeoffs, and a decision hint.
| Name | Required | Description | Default |
|---|---|---|---|
| products | Yes | Product titles or SKUs to compare (e.g. ['MIRA', 'CryoSculpt']) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the return shape: 'Returns structured comparison with shared attributes, differences, tradeoffs, and a decision hint.' It also implies a non-destructive read operation consistent with the readOnlyHint annotation. It goes beyond the schema by outlining the analytical output.
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 compact: two sentences that front-load the core action and immediately follow with usage guidance. No 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?
For a single-parameter tool with no output schema, the description covers purpose, usage conditions, alternatives, and the nature of the output. It provides enough context for an agent to select and invoke the tool correctly.
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?
The schema already provides a detailed description for the 'products' parameter (product titles or SKUs, minItems=2, maxItems=5). The tool description reinforces the 'two or more' requirement but does not add significant new semantics beyond the schema.
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 uses the specific verb 'compare' with the resource 'products' and specifies 'side by side', clearly distinguishing it from single product lookup by naming get_product as the alternative.
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?
It explicitly states when to use ('when the user asks to compare, says X vs Y, or wants to decide between options') and when not to ('Do not use for single product lookup — use get_product'), providing clear 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.