judge_options

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

Annotations already cover the safety profile (readOnly/idempotent/non-destructive/openWorld), and the description adds substantial non-obvious context: the 법제처 API (LAW_OC) credential requirement, a guarantee that article text is verbatim from the in-force version and never fabricated, the verdict-classification rules, the 0.3-overlap guard that downgrades '모순' to '판단 필요', and the caveat that '판단 필요' is a textual-comparison result rather than legal advice.

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 definition is long but front-loaded with the purpose and organized under clear labels (언제/입력/처리/반환/판정), so it is scannable rather than rambling. The '처리' section exposes internal pipeline steps (law.version → law._full_units → claims.judge) that are the least essential part for a caller, but the rest of the sentences each carry 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?

With no output schema available, the description carries the full burden of describing the return shape (법령/시행본/후보조/보기별 fields/요약/주의) and it does so in detail, alongside the verdict taxonomy and the reading caveat. For a 6-parameter, multi-step judgment tool, nothing an agent needs to call and interpret it correctly 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% and the description's '입력' section largely mirrors the schema text (statement id ordering, articles format, as_of default, also_decree candidate expansion), so it adds little meaning beyond what the structured fields already state. Baseline 3 applies when the schema does the heavy lifting.

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 verb, resource and scope: batch character-level comparison of multiple statements/claims against 1–5 articles, returning a per-statement table of best-matching article and verdict. It also explicitly distinguishes itself from the sibling verify_statement ('보기마다 verify_statement를 따로 부르지 않고 한 번 호출로 처리'), so an agent can route between them without opening either schema.

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 '언제' section gives an explicit trigger condition (multiple options/statements to compare against a specific 1–5 articles at once) and names the alternative approach (calling verify_statement per statement), including why this tool replaces that pattern. No inference is required to decide when to use it.

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