get_executive_compensation

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

No annotations are provided, so the description must carry the behavioral disclosure burden. It does describe output sections and parameter defaults, but it contradicts itself: the first paragraph says '5개 섹션을 반환합니다' while the Returns section says '임원 보수 4섹션 텍스트'. This makes the actual return behavior unreliable, and it does not state read-only semantics, data source, or error 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?

The description is reasonably short and organized with Args/Returns sections. However, it is not fully polished: the Returns line is redundant with the first paragraph, and the 5-section vs 4-section discrepancy creates confusion. The structure is acceptable but not tightly edited.

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 3-parameter read-style tool, the description is mostly complete: it covers all parameters, defaults, allowed report types, and output content. Yet the contradictory section count and the absence of guidance on when to choose this tool over siblings such as track_insider_trading leave gaps. The presence of an output schema reduces the need to explain return values, but the description's own return statement is inconsistent.

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 description coverage is 0%, and the description compensates by explaining all three parameters: company_name accepts company name or stock code, year is the business year with a previous-year default, and report_type is enumerated as annual/half/q1/q3. This is substantial added meaning beyond the bare schema. Minor ambiguity remains around the exact year format and the mismatch between the schema's empty-string default and the described previous-year default.

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 states a specific action with a clear resource: '임원 보수 현황을 조회합니다' (query executive compensation status) and adds investigative context as reference material for unfair-trade detection. It is distinguishable from siblings by the resource, but it does not explicitly contrast it with overlapping tools like track_insider_trading.

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 phrase '(불공정거래 탐지 참고 자료)' implies the tool is intended for unfair-trade detection investigations, giving some context for when to call it. However, it does not provide explicit when-to-use/when-not-to-use conditions or name sibling alternatives, so selection guidance is mostly left to inference.

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