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

With no annotations, the description carries full behavioral burden and does so strongly: it states the operation is deterministic, that no LLM is involved, what the result will contain, and that low a_coverage/b_coverage signals thin knowledge and must be reported honestly rather than fabricated.

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?

Every sentence earns its place: purpose, invocation condition, return structure, determinism, and honesty policy. The most decision-relevant information is front-loaded before the examples, and there is no filler.

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?

The tool has a rich output schema, so return values need not be fully re-specified; the description still summarizes them. It also covers when to use it, when not to, input flexibility, determinism, and how to handle low coverage—leaving no practical gap for an agent selecting or invoking this tool.

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% and the schema already documents a ('A vs B' can be passed at once) and b (optional when a contains vs). The description adds contextual examples but does not materially deepen parameter semantics beyond the schema, so 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 description states a specific verb ('대조한다' – contrasts) and a specific resource ('두 공법·개념의 차이'), and distinguishes itself from scenario by explicitly assigning single sequence to scenario. Concrete examples make the matching intent unambiguous.

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?

It gives explicit call conditions ('비교/차이 요청에 호출하라') and an explicit exclusion ('단일 순서는 scenario'), naming the sibling tool that should be used instead. The examples make positive matching easy.

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