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

Beyond the readOnly/idempotent annotations, the description discloses the provisional-year caveat, that listed_konex can legitimately return empty financial results, and that audit_only data must be fetched via download_document rather than get_financials. It also explains how data_location values map to data sources, which materially affects follow-up behavior.

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 long, the description is organized into Purpose, Usage, Response, and Rules sections with bullet-style entries, making it easy to parse. Every block earns its place: routing rules, taxonomy, and examples are all decision-relevant, and the one-sentence summary is front-loaded before the detailed sections.

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?

There is no output schema, so the description compensates by enumerating the key data_location and corp_cls values and their meanings. It also covers edge cases (year omitted, 비상장 vs 상장, listed_konex empty results) and names the follow-up tools, making the tool safely invocable without external knowledge.

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 already documents both parameters comprehensively (100% coverage), and the description adds value with concrete usage examples and the year-sensitive routing nuance (year matters only for 비상장사). It does not invent new semantics, but it reinforces and contextualizes the schema rather than merely repeating it.

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 sentence names the specific resource (기업개황) and two concrete outputs (basic company profile fields and data_location), using a clear verb (조회). It also explicitly distinguishes itself from get_audit_profile and find_corp_code, so an agent can separate it from 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 [Purpose] section states the intended context ('routing check before 재무·감사 calls on unfamiliar, especially 비상장, companies') and [Rules] gives explicit routing decisions based on data_location, including when not to retry. The three usage examples translate natural language questions into concrete parameter settings, leaving no ambiguity about when and how to call it.

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