get_product_image슈퍼쇼핑 상품의 대표 이미지를 실제 이미지(렌더 가능)로 반환.
언제: 사용자가 상품 사진을 보고 싶어할 때("이 코트 보여줘"). 검색/추천 결과의 thumbnail은 URL(텍스트)이라 화면에 안 뜨므로, 시각 확인이 필요하면 이 툴로 이미지 자체를 가져온다. 반환: 이미지 콘텐츠(여러 개를 한 번에 보여주지 말고 관심 상품 위주로 호출).
| Name | Required | Description | Default |
|---|---|---|---|
| product_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive. The description adds value by specifying the return is actual image content (not URL) and advising usage pattern. No contradictions.
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?
Two short paragraphs with essential info front-loaded. Each sentence adds value, though could be slightly more streamlined.
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 simple read tool with one parameter and no output schema, the description adequately explains return type (renderable image) and usage context. Slightly lacking return format details.
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?
Only one parameter product_id, which is self-explanatory by name. Schema coverage is 0%, but the description does not add format or constraint details beyond the name. Minimum viable.
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 it returns the actual renderable image for a SuperShopping product, distinguishing it from sibling tools like search_products that provide URL thumbnails.
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?
Provides explicit when-to-use: when user wants to see product photos. Offers practical advice to focus on interested products rather than showing multiple simultaneously. Lacks explicit when-not or 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.
Each tool has a clearly distinct purpose: market analysis, product comparison, detail retrieval, image fetching, checkout initiation, outfit recommendation, general recommendation, and search. Descriptions and usage contexts disambiguate effectively.
All tool names follow a consistent verb_noun pattern (e.g., analyze_market, compare_products) using lowercase with underscores, making them predictable and easy to differentiate.
With 8 tools covering search, recommendation, comparison, details, images, market analysis, and checkout, the count is well-scoped for a comprehensive shopping assistant without being overwhelming.
The tool surface covers the main shopping workflow—search, recommend, compare, view details, images, market analysis, and checkout. Minor gaps like category listing or user-specific features are absent but not critical for the core purpose.