get_individual_compensation_amount

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It fails to describe any behavioral traits: it doesn't indicate if this is a read-only query, what data format it returns, whether it requires authentication, or if there are rate limits. The abstract analytical language offers no practical insight into how the tool behaves when invoked.

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 a single, dense sentence in Korean that packs abstract concepts but lacks clarity. While concise in length, it's inefficiently front-loaded with analytical jargon rather than stating the tool's function upfront. It could be restructured to prioritize actionable information.

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?

Given the tool has 3 parameters, no annotations, and no output schema, the description is incomplete. It fails to address key contextual needs: what the tool returns (e.g., compensation amounts, risk scores), how it differs from similar tools, or any behavioral constraints. For a tool with no structured support beyond input schema, the description leaves significant 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?

Schema description coverage is 100%, with clear descriptions for corp_code, bsns_year, and reprt_code. The description adds no parameter semantics beyond what the schema provides—it doesn't explain how these inputs relate to 'compensation imbalance analysis' or provide usage examples. With high schema coverage, the baseline is 3, as the description doesn't compensate but doesn't detract either.

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 '고액 수령자 중심 임원 보수 정보를 통한 보상 불균형 및 지배구조 리스크 분석' (Analysis of compensation imbalance and governance risks through high-earner executive compensation information) is vague and abstract. It describes an analytical outcome rather than stating what the tool actually does (e.g., retrieves, calculates, or lists specific compensation data). It restates concepts from the tool name 'get_individual_compensation_amount' without clarifying the action.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention any sibling tools (e.g., get_individual_compensation, get_executive_compensation_approved, get_total_compensation) or specify contexts where this tool is appropriate. There are no prerequisites, exclusions, or comparisons to help an agent select it correctly.

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