우리 지역 육아 지원 서비스 찾기(참고용)

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

The annotations already declare that the tool is read-only, idempotent, and open-world, so the description does not repeat them. Instead, it adds real operational context: it discloses the upstream data source, warns that information is 참고용 and that conditions and amounts must be verified with the relevant 지자체, and states the prerequisite API key (DATA_GO_KR_SERVICE_KEY) and dataset approval (15108347) needed before calling it. It does not describe pagination or failure behavior for an unapproved key, so this falls short of a 5.

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 core capability is front-loaded in the first sentence, followed by the returned fields, the accuracy disclaimer, and the prerequisites; each sentence carries information. The wording is slightly dense but free of filler.

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?

For a four-parameter, read-only search tool with no output schema, the description covers the data source, the returned fields, the accuracy caveat, and the credential/dataset prerequisites. The remaining gap is sibling routing against find_benefits and find_daycare, which is the only thing an agent might still get wrong.

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%: region, stage, keyword, and limit are already fully documented with format and examples in the schema. The description adds no parameter-specific meaning beyond restating the region and stage scope, so the baseline of 3 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?

The description gives a specific verb and resource: it searches 임신·출산·영유아 지원 서비스 for a given 시도/시군구 from the 지자체복지서비스 dataset, and it lists the returned fields (서비스명, 소관 부서, 수정일, 원문 요약, 복지로 링크). It does not, however, distinguish this tool from similarly-named siblings such as find_benefits or find_daycare, so an agent may still hesitate over which one to call for a childcare/welfare query.

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?

Usage context is implied (finding local childcare support services by region and stage), and a caveat tells the user to confirm eligibility and amounts with the local government. There is no explicit statement of when to prefer this over find_benefits or find_daycare, and no guidance on exclusions or on when results may be empty.

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