na_detail

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

Beyond the readOnlyHint/openWorldHint annotations, it discloses measured operational traits: the returned field set is identical to na_search (redundancy), each call consumes daily quota (10,000/day), nonexistent control numbers still return ERR04 making them indistinguishable from transient errors, and access requires a separate data.go.kr application. This is exactly the class of behavioral context annotations cannot carry.

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?

Although longer than typical descriptions, every sentence earns its place: purpose is front-loaded, the critical redundancy caveat follows immediately, then error and auth warnings. Structured formatting (bold, emoji markers, bullet separations) makes the dense content scannable, and there is no filler or 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?

For a single-parameter read-only lookup with no output schema and minimal annotations, the description is complete: purpose, parameter origin, output equivalence (which doubles as return-value documentation since fields mirror na_search), error semantics, quota impact, and access prerequisites are all covered. The empirical field-count breakdown per document type reinforces the redundancy claim without leaving 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?

With 0% schema description coverage, the description fully compensates: it defines controlno as the control number taken from na_search results and supplies two concrete format examples (MONO12026000012887, KINX2026037525). This gives the agent both the provenance and the shape of the only parameter.

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 opening line states a specific verb and resource: '제어번호 1건의 서지정보를 조회한다' (retrieves bibliographic information for one control number). It further distinguishes from siblings by asserting the field set is empirically identical to na_search's raw output, while na_toc is named as the tool that returns genuinely new content.

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 is exemplary on this dimension: explicit when-to-use ('쓸 자리는 제어번호만 아는 자료를 조회할 때'), explicit when-not-to-use ('이미 검색한 자료라면 이 도구를 부를 이유가 없다'), and a named alternative for a specific need (na_toc for table-of-contents text). No inference is required from the agent.

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