check_completeness

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

Annotations already declare readOnly and idempotent behavior, and the description adds meaningful context: the two-level comparison logic, the treatment of aggregation as normal, and the response shape (L3 finding list with empty report coordinates and populated source coordinates). There is no contradiction with the annotations.

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 purpose is front-loaded in the first sentence, and each section—rationale, methodology, return shape, and examples—serves a clear function. The motivational paragraph is slightly longer than strictly necessary but still earns its place by explaining why this tool matters.

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 read-only comparison tool with annotated safety, complete schema descriptions, defaults, and an output schema, the description covers selection, invocation, interpretation, and return format. Nothing needed to call or interpret the tool is missing.

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%, so the schema already documents the roles of 보고서, 전주차, 기준주차, and 원본디렉터리. The description confirms the report-vs-source comparison but adds little parameter-level detail beyond what the schema provides, so the baseline 3 applies.

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 first sentence states a specific action: checking whether judgments that should appear from the source are missing from the report, and labels the level (L3). The examples and the explicit pointer to verify_claims distinguish this from the nearest sibling, so an agent can identify it as the omission-checking tool.

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 gives an explicit use case ('빠뜨린 지연 과제가 있나요?') and an explicit exclusion: if checking whether written values are correct, use verify_claims. It also explains that aggregated reporting without individual items is normal, which clarifies when this tool should and should not flag an omission.

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