공공데이터 서비스·제품 설계

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is known. The description adds valuable context by clarifying that it draws from a static list and does not query live data, giving the agent a clear mental model of the tool's behavior beyond the annotations. 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?

The description is a single, well-structured Korean sentence that states the core function and a critical limitation, with no redundancy or filler. It is front-loaded with the primary action and includes an important boundary in the same sentence.

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 five parameters and no output schema, the description should explain how inputs influence the result and what the reference design proposal looks like. It mentions the content areas (product composition, data flow, verification procedures, expected outputs) but gives no details on parameter usage or output format, leaving significant gaps for an agent to invoke the tool correctly.

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 schema lists five parameters (goal, limit, regions, data_needs, desired_outputs) with 0% description coverage. The description makes no mention of any parameter, leaving the agent without any guidance on what values to provide or how the parameters shape the design. The description fails to compensate for the lack of schema coverage.

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 creates a reference design proposal for product composition, data flow, verification procedures, and expected outputs, based on a static list of data types and official providers. The verb '만듭니다' (creates) is specific, and the resource is clearly '공공데이터 서비스·제품 설계' (public data service/product design). This distinguishes it from sibling tools that list, search, or recommend data combinations.

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 implies usage for design tasks rather than actual data retrieval, especially with the note '실제 데이터는 조회하지 않습니다' (doesn't query actual data). However, it does not explicitly mention alternatives or state when to use this tool over others, leaving the guidance implicit rather than explicit.

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