get_executive_compensation_approved

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description mentions '리스크 분석' (risk analysis) which suggests analytical processing, but doesn't clarify whether this is a read-only data retrieval tool, what format the output takes, whether it requires authentication, or any rate limits. For a tool with no annotation coverage, this minimal description fails to provide essential 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?

While the description is technically concise (one sentence), it's under-specified rather than efficiently informative. The single sentence is abstract and doesn't front-load practical information about the tool's function. It fails to earn its place by providing actionable guidance to an AI agent.

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 complexity implied by the tool name (executive compensation approval data), no annotations, and no output schema, the description is inadequate. It should explain what data is returned, in what format, and how it differs from related compensation tools. The abstract analytical framing doesn't provide the completeness needed for an AI agent to understand and use this tool effectively.

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 all three parameters clearly documented in the input schema. The description adds no parameter-specific information beyond what's already in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline score is 3 even with no parameter information in the description. The description doesn't compensate for any gaps because there are none in the schema.

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 '주총 승인 보수 한도를 통한 보상 지배구조 및 집행 투명성 리스크 분석' (Analysis of compensation governance and execution transparency risks through shareholder-approved compensation limits) is vague and abstract rather than stating a clear action. It describes an analytical purpose rather than specifying what the tool actually does (likely retrieves executive compensation approval data). This is essentially a tautology that restates the tool name 'get_executive_compensation_approved' in analytical terms without providing operational clarity.

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 absolutely no guidance on when to use this tool versus alternatives. With multiple sibling tools related to executive compensation (get_executive_compensation_by_type, get_individual_compensation, get_total_compensation, get_unregistered_exec_compensation), there is no indication of how this tool differs or when it should be preferred. The description fails to establish any context for appropriate usage.

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