get_user_looksFetch the user's saved virtual try-on looks (avatar images generated from their selfie). Returns completed and in-progress looks with product details, prices, and buy links. Use this to show the user what outfits they have already tried on.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of looks to return per page. Defaults to 10. | |
| offset | No | Pagination offset (number of looks to skip). Defaults to 0. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare read-only and idempotent, and the description adds behavioral content: it returns both completed and in-progress looks and includes product details, prices, and buy links. It explains what the caller will get without contradicting annotations.
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?
Three sentences, each with a distinct job: identify resource, specify returned fields, state use case. No filler or redundancy.
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 low-complexity read-only tool with optional pagination params documented in the schema, the description covers the returned content and the intended use. It doesn't detail response envelope or ordered results, but nothing critical is missing.
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 description coverage is 100%, and both limit and offset have descriptions in the schema. The tool description adds no parameter-level semantics, so baseline 3 applies.
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?
States a specific verb ('Fetch') and resource ('user's saved virtual try-on looks'), and clarifies it is about looks already tried on, distinguishing it from try-on or product search tools. It also lists what is returned, making the tool's role unambiguous.
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?
Explicitly gives a use case: 'show the user what outfits they have already tried on.' This supplies clear context but does not name sibling alternatives or exclusions, so it falls short of full when-to-use/not guidance.
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 target a clear resource (search, details, mix-and-match, wishlist, try-on), but search_fashion_products explicitly claims all styling/outfit requests while get_mix_and_match also handles outfit building, and both list 'what goes with my blue jeans' as an example. The selfie upload guidance also conflicts with try_on_product's built-in upload widget. Still, the detailed descriptions narrow most boundaries.
All names use snake_case and a verb-first pattern (get_, request_, search_, show_, try_on), making the set predictable. Minor inconsistency: get_mix_and_match and get_user_looks are less noun-like than the rest, and the retrieve-style verbs vary between get, show, and request.
8 tools is within the ideal 3-15 range and maps naturally to a shopping/styling assistant: search, detail, outfit, try-on, wishlist, saved looks, and uploads. No tool feels redundant enough to cut, and adding more would risk bloat.
The set covers the main shopper journey: search/browse, product detail, outfit building, virtual try-on, saved looks, and wishlist. The main gaps are wishlist mutation (add/remove) and an explicit way to manage/inspect selfies or context images, but these are workable around the existing tools.