회사·기업집단 식별

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

With no annotations, the description carries the full burden and excels: it discloses ambiguous results (status="ambiguous" with candidates), the need to re-call with corp_code, API call costs for fetchJurirNo (1 DART call) and includeGroup (~103 portal calls first time), the EGROUP_API_KEY requirement, and 1-year caching. This is rich, honest behavioral context.

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 longer than two sentences but every sentence earns its place—purpose, usage positioning, caveat, and parameter-specific side effects. It is well-structured and front-loaded with the core functionality, though the parenthetical examples make it slightly dense.

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 tool with 5 parameters, no output schema, and non-trivial external API behavior, the description covers everything needed: input types, output fields, ambiguity resolution, auth requirements, call costs, and caching. It even mentions status="ambiguous", which hints at the return structure. The only minor gap is the explicit success response shape, but the described fields are sufficient.

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% with each parameter described, so baseline is 3. The description adds meaningful context beyond schema: it explains why fetchJurirNo is needed for portal integration, quantifies the call cost of includeGroup, and describes the ambiguity behavior tied to the query parameter. This added semantic depth justifies a 4.

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?

States a specific verb ('풀어줍니다' / resolves) and the exact input types (회사명, 종목코드, 법인코드, 법인등록번호, 기업집단명) and outputs (corp_code, stock_code, 법인등록번호, 소속 기업집단). It positions itself as the prerequisite step before other tools, clearly distinguishing its role from siblings.

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 instructs to call this tool before using other tools to specify a company, and gives a follow-up instruction for ambiguous cases (call again with corp_code). However, it does not name specific sibling tools or state when NOT to use this tool, so it stops short of full alternative guidance.

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