compare_products슈퍼쇼핑 상품 여러 개(2~5개)의 스펙·가격 비교 매트릭스 + 한 줄 결론.
언제: 사용자가 후보 여러 개를 견주려 할 때("이 중에 뭐가 나아?"). product_ids는 search 결과에서. 반환: {attributes: 공통 키, matrix: 상품별 값/가격, verdict: 한국어 결론}.
| Name | Required | Description | Default |
|---|---|---|---|
| product_ids | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readonly, idempotent, non-destructive. The description adds concrete behavior: returns a matrix with attributes, values, prices, and a Korean verdict, and limits input to 2-5 products. No contradiction.
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 sentences plus a usage hint, all front-loaded. No wasted words.
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?
With an output schema (context signal), the description adequately covers input, output structure, and usage context. Could mention return format more explicitly, but sufficient for a comparison tool.
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_ids array) with 0% schema description coverage. Description adds that IDs come from search results, which provides some context, but lacks detail on array size constraints or format. Baseline for coverage 0% is low; description partially compensates.
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 compares 2-5 products and returns a spec/price matrix with a verdict. This distinguishes it from siblings like search_products (searching) or get_product_detail (single product).
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 guidance ('when user wants to compare multiple candidates') and notes product_ids come from search results. Does not explicitly mention when not to use, but the context is clear.
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.