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

Annotations declare readOnlyHint=true and openWorldHint=true, indicating a safe read operation. The description adds that the output is a comparison table including price range and representative mall, giving useful behavioral context 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?

Two concise sentences, front-loaded with the action and outcome. The first sentence explains the functionality and the second gives a clear usage example. No wasteful repetition of schema details.

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 description explains the tool's purpose, usage, and output structure (comparison table with specific elements). Annotations cover safety, and schema covers parameters. It lacks only nuances like pagination or edge cases, but for a read-only comparison tool, it is sufficiently 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 100%, with each parameter (queries, sort, perQuery) having its own description. The tool description does not add extra parameter semantics beyond what the schema already provides, which is the baseline of 3.

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 explicitly states the tool searches multiple candidate queries (2-5) and displays a comparison table of lowest price, price range, and representative shopping mall. This distinctively separates it from sibling tools like search_products (single search) or shopping_trends, satisfying 'specific verb+resource+scope'.

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?

It clearly instructs to use for structured comparisons like 'A vs B', providing concrete usage context. It does not explicitly name alternatives or exclusions, but the comparison use case is sufficiently clear and differentiates 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?

The description adds behavioral context beyond annotations by explaining the result selection logic (most recent or specified by resultId) and the file saving action. However, it does not disclose potential side effects such as overwriting existing files, behavior when no result exists, or return values. Annotations only provide readOnlyHint=false, which is already implied by 'saves', so the description adds limited new behavioral detail.

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 long, with the first sentence front-loading the core function and the second providing the intended use case. Every word is purposeful, with no redundant or vague phrasing, making it highly concise and well-structured.

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 description covers the tool's purpose, result selection logic, output formats, and use case, which is sufficient for an agent to understand how to invoke it. It lacks details about error handling, return values, or overwriting behavior, but given the simple nature of the tool and the comprehensive schema descriptions, the description is adequately 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?

The input schema already provides 100% coverage with descriptions for all three parameters, including format, filename, and resultId. The tool description mentions '직전(또는 resultId로 지정한)' which aligns with the resultId schema description, but does not add any additional parameter semantics beyond what the schema already states.

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 states a specific verb ('저장합니다' - saves) and resource (search/comparison/trend results) to a local file, with explicit formats (markdown or JSON). This clearly distinguishes it from sibling tools like search_products, compare_products, and shopping_trends, which generate results rather than save them.

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 '위시리스트/기록 용도로 사용하세요' (use for wishlist/record purposes), giving a clear context for when to use the tool. It does not explicitly mention when not to use it or name alternatives, but the intended use case is specific and helpful.

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=true and openWorldHint=true, covering safety and scope. The description adds behavioral detail by specifying the exact output format (markdown table with listed fields) and official API nature, which is useful beyond the annotations. 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 sentences, front-loaded with the action and resource, and no filler. Every word adds value, explaining what it does, what it returns, and when to use it.

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 8 parameters and no output schema, the description covers the return format and usage context well. It does not discuss pagination or filter combination, but the schema covers parameter details, and the description is sufficiently complete for a search 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?

Schema description coverage is 100%, with all parameters (query, sort, limit, start, exclude, maxPrice, minPrice, naverPayOnly) having descriptive Korean labels in the schema. The description does not add parameter-level meaning, but the schema already provides it, so the baseline score 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?

Specific verb '검색합니다' (search) identifies the action, and the description lists return fields (상품명·최저가·쇼핑몰·카테고리·링크) as a markdown table. It clearly differs from siblings like compare_products and shopping_trends by focusing on general search and initial exploration.

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 states '가격 비교와 초기 탐색 단계에서 사용하세요' (use for price comparison and initial exploration), giving clear context for when to use. It does not mention when not to use or name alternatives, but the context is sufficient.

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?

The read-only annotation is already present, but the description adds valuable behavioral context: the output is normalized 0-100 relative trend, not absolute values. This helps prevent misuse and goes 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-structured sentence with a clear caution. It is front-loaded, concise, and every word adds value without repetition.

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 rich schema and read-only annotation, the description is fairly complete. It explains the core behavior and caveat. It doesn't mention response format or limits, but these are not critical given the schema clarity and the tool's simplicity.

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 100%, so the baseline is 3. The description does not add parameter-specific details beyond the schema; it only mentions that categories are queried, but the schema already covers all parameter meanings.

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 queries Naver DataLab Shopping Insight for category-specific relative trend trends normalized 0-100. It explicitly differentiates from absolute sales volume and real-time popular search ranking, distinguishing it from potential sibling tools.

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 clear context: use this for relative category trends over a specified period. It warns against misinterpreting as absolute sales or real-time rankings, which implies when not to use it, though it does not explicitly name alternative tools.

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