analyze_market슈퍼쇼핑 카테고리 시세 분석 — 가격 분포·최저가몰·대안.
언제: 사용자가 시세/적정가를 물을 때("그래픽카드 요즘 얼마", "노트북 가격대"). category는 이름(예: '노트북') 또는 ID. 반환: 가격 통계(최저/중앙/최고·분포)·최저가 쇼핑몰·대안 상품.
| Name | Required | Description | Default |
|---|---|---|---|
| category | Yes | ||
| attributes | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable context about the return data (price statistics, lowest price mall, alternative products), enhancing transparency beyond 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?
The description is extremely concise: a single line for purpose and a single line for usage/output. No unnecessary words, and the key information is front-loaded.
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?
Given the presence of an output schema (which handles return value details), the description adequately covers the tool's purpose, when to use, and what it returns. It could mention potential errors or edge cases but is generally complete.
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 0%. The description only adds meaning for the 'category' parameter (name or ID), but the 'attributes' parameter is not mentioned. This incomplete coverage results in a score of 2.
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 analyzes Super Shopping category market prices, including price distribution, lowest price mall, and alternatives. It uses specific verbs and resources (analyze market) and distinguishes from sibling tools like compare_products and search_products.
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 explicitly states when to use the tool (when users ask about market prices or fair prices) with concrete examples. However, it does not specify when not to use it or mention alternative tools, so it scores 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.