get_statute_frequency

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. It fails to state whether the operation is read-only, what dimension of frequency is counted (per exam, per question, aggregated across all exams), what the response shape is, or how the optional exam_code changes the result. For a data-retrieval tool, this is a significant transparency gap.

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 compact sentence with no waste. However, this brevity is closer to under-specification than earned conciseness — the few words present do not carry enough information to justify the size.

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 one-parameter tool with no annotations and no output schema, the description was the last line of defense for the agent. It leaves open what the output contains, how exam_code filtering works, and how this tool relates to overlapping siblings. An agent would likely have to call the tool blindly to learn these details.

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 0%, so the description must compensate. It only repeats that exam_code is optional (可選), which is already visible in the schema's default: null. It does not explain what an exam_code is, where to obtain valid values (e.g., from list_exams), or how the filter affects the aggregation.

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 "法條考頻統計（可選 exam_code）" clearly states the tool's resource (statute/legal provision exam frequency) and action (statistics/aggregation). The purpose is understandable on its own, though it does not differentiate from siblings such as get_topic_distribution or get_issue_distribution, which are also statistical reporting tools.

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 only usage hint is "可選 exam_code" (optional exam_code), which tells the agent a filter exists but provides no guidance on when to choose this tool over the many sibling statistical/reporting tools (get_topic_distribution, get_issue_distribution, get_exam_map). No exclusions or alternative routing is given.

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