supershopping

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context, specifying the return values (price statistics, lowest price mall, alternatives) and that the tool is for analysis, not mutation. 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?

The description is short and well-structured: a header line summarizing the function, a 'When' line for usage context, and a 'Returns' line for output. Every sentence adds value with no 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?

Given the output schema exists, the description need not detail return structure, yet it still summarizes returns. It covers the main use case and required parameter, but misses the optional 'attributes' parameter. Overall fairly complete for its complexity.

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%, so description must carry the burden. It explains the 'category' parameter (name or ID) but completely omits the 'attributes' parameter, which is optional but undocumented. This is a significant gap.

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 performs market price analysis for a category, returning price distribution, lowest price mall, and alternatives. It distinguishes from sibling tools like compare_products and get_product_detail by focusing on market-level analysis rather than individual product comparisons.

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 provides usage context with examples of when to use ('when user asks about market prices/fair prices'). Lacks explicit exclusions or alternatives, but the context is clear enough for an agent to differentiate from siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral details: product count limit (2-5), return structure (attributes, matrix, verdict), and source of product_ids. 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?

The description is very concise: three sentences covering purpose, usage, and return value. No unnecessary words, front-loaded with key information.

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 simple tool, annotations, and presence of output schema, the description covers inputs, outputs, and usage context completely. No gaps.

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 coverage is 0%, so description must compensate. It explains that product_ids are required and should come from search results, adding meaning beyond the schema. Could explicitly state the array size limit (2-5) but overall helpful.

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 multiple products (2-5) and produces a matrix and verdict. It distinguishes itself from siblings like get_product_detail (single product) or search_products (search).

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 says when to use ('when user wants to compare candidates') and notes product_ids come from search results. It lacks explicit 'when not to use' or alternative suggestions but provides clear context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds value by detailing the output structure: attributes, offers with buy_urls, comparison_url, other_sources for new/used price spread, and affiliate disclosure. This is beyond what annotations provide.

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 a single well-organized paragraph with bullet points, using no unnecessary words. Every sentence adds value (when to use, what is returned).

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?

The tool has one required parameter, comprehensive annotations, and an output schema. The description covers purpose, usage context, and return details. It lacks mention of error handling or edge cases (e.g., invalid product_id), but for a read-only detail tool this is acceptable.

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?

With only one parameter and 0% schema description coverage, the description partially compensates by explaining where to obtain product_id (from search/recommend), but does not provide format, validation rules, or examples. Minimal added semantics.

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 provides full details of a product including specs, per-store prices, and new/used cross-comparison. It distinguishes from sibling tools like search_products or compare_products by focusing on a single product's deep detail.

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 says when to use (when user is deep into a product or about to purchase) and how to get the product_id (from search/recommend results). It does not explicitly state when not to use, but context with siblings makes it clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds that the return is actual image content (renderable) and advises against calling many at once. This adds useful context beyond annotations without 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?

The description is two sentences, front-loading the purpose and then giving usage guidelines. Every sentence adds value with 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?

The tool is simple with one parameter and no output schema. The description covers purpose, when to use, and a usage caveat, making it complete for the tool's complexity and context signals.

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 coverage is 0% and the description does not explicitly describe the product_id parameter. However, the parameter name is self-explanatory given the tool name, and the description implies it identifies the product. For a single simple parameter, this is adequate but not exemplary.

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 returns the actual image (renderable) of the product's main image, using a specific verb '반환' (return) and resource '슈퍼쇼핑 상품의 대표 이미지'. It distinguishes from sibling tools like get_product_detail or search_products which are text-based.

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 provides explicit when-to-use guidance: '사용자가 상품 사진을 보고 싶어할 때' and explains why default thumbnails are insufficient (URLs not rendered). It also gives a usage caveat: '여러 개를 한 번에 보여주지 말고 관심 상품 위주로 호출', helping the agent avoid overuse.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Disclosures beyond annotations: actual payment occurs on merchant page, need to show affiliate disclosure, and special handling for used items. No contradictions with 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?

Well-structured with front-loaded purpose, usage timing, return format, and special cases. Slightly verbose but each sentence adds value.

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?

Covers return format, affiliate disclosure, and used item handling. Missing parameter details but overall sufficient for a checkout tool with annotations and output schema.

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%, but description does not explain parameters (product_id, quantity) beyond mentioning 'selected product'. Lacks detail on how to specify product or adjust quantity.

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?

Clearly states the tool initiates checkout for selected products, returning purchase links and pricing. Distinguishes from sibling tools like search_products or compare_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?

Explicitly specifies when to use (when user shows purchase intent) and what it does not do (actual payment on merchant page). Also provides guidance for used items.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnly, idempotent, non-destructive. The description adds valuable behavioral context: it returns structured outfit data with items, links, and instructions to use get_product_image for photos. No contradiction with 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 concise, structured with clear sections (purpose, when, parameters, return), and every sentence adds value. No redundancy or fluff.

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 output schema exists, the description provides sufficient context on return structure (seed, items with slots, styling_reason, affiliate_disclosure). It also notes image handling. Minor lack of detail on slot semantics, but overall complete for the tool's complexity.

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 has 0% description coverage; the description compensates by explaining each parameter's meaning and constraints: product_id (seed item), intent (mood), budget (total upper limit), and the requirement that at least one of product_id or intent is needed. This adds critical information 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 clearly states the tool recommends outfits (coordination) based on a seed product or intent, using specific verb '추천' (recommend) and resource '코디' (outfit). It distinguishes from sibling tools like recommend_products (single products) by focusing on full outfit composition.

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 '언제' section provides clear examples of when to use (e.g., matching a coat, fall date look), giving context. However, it does not explicitly state when not to use or mention alternatives, though the sibling list and purpose imply differentiation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations (readOnlyHint, idempotentHint, destructiveHint) already provide safety guarantees. The description adds the return format and mentions affiliate disclosure, but does not elaborate on other behaviors like data freshness, error handling, or rate limits. With annotations covering the baseline, a 3 is appropriate.

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 structured with clear sections: what, when, arguments, return format. While concise overall, it uses bullet-like formatting and some verbosity (e.g., 'affiliate_disclosure?' with a question mark). It could be tightened, but is generally well-organized.

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?

The tool has an output schema and 4 parameters, one required. The description provides a summary of the return format but does not fully detail all output fields or edge cases. With output schema existing, the description doesn't need to repeat everything, but it omits some details (e.g., limit parameter, behavior when budget is null).

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% for parameters, but the description explains 'intent' and 'budget' as 용도/취향 and 예산 상한, and 'must_have' as 필수 조건. However, 'limit' is not explained. This partial coverage raises the score above a 2 but not to 4, as some guidance is missing.

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 specifies the tool's core function: recommending products based on intent and budget, with reasoning. It clearly distinguishes from sibling tools like search_products by noting this is for vague queries, not specific search terms.

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 the user has a vague intent or budget without a specific search query. It contrasts with search queries but does not explicitly name alternatives or state when not to use, which keeps it from a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. Description adds behavioral details like automatic extraction of domain, gender, price range from query, and the return format. It also notes to use get_product_image for photos. However, it does not mention pagination behavior or performance considerations.

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 concise, front-loaded with key information, and uses bullet points for clarity. Every sentence adds value without 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?

Covers usage, examples, alternative tool, and return format sufficiently given the output schema exists. Missing details on pagination and limit parameter, but overall complete for a search tool with good annotations.

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 coverage is 0%, so description must explain parameters. It mentions automatic extraction but does not clarify how explicit parameters (limit, category, min_price, etc.) interact with this extraction or what values they accept. The description leaves ambiguity about parameter usage.

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 does natural language search for new and used products, provides examples, and distinguishes itself as the basic entry point. It also mentions the alternative tool recommend_products for recommendations.

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 says 'When: user wants to find specific products' and provides examples. Clearly states to use recommend_products when only recommendations needed or query is ambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.