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

With no annotations, the description carries the full burden. It discloses the returned content categories (pass rate vs baseline, failure deficits, center rankings), which adds useful behavioral context, but it does not mention data freshness, source limitations, authentication, or any other operational traits.

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?

A single, front-loaded sentence that packs the specific resource and key return dimensions into a parenthetical without waste. Every phrase earns its place.

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 description is reasonably complete for a no-argument reporting tool but leaves some gaps: no output schema exists, and the description does not specify aggregation period, geographic scope, or data format. It conveys enough for an agent to understand the tool's purpose but not enough to anticipate the shape of the response.

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?

The tool has zero parameters, so the baseline score is 4. No additional parameter semantics are needed or present.

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?

States a specific verb (Retrieve) and resource (official traffic department driving license exam outcomes), with concrete details about what the outcomes include: pass rate vs national baseline, failure hurdle deficits, and center rankings. It is clear what the tool does, though it does not explicitly differentiate itself from siblings like get_platform_stats.

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?

Provides no guidance on when to use this tool versus alternatives such as get_platform_stats or get_top_mistakes. The description implies usage by naming the specific data domain, but there are no explicit when-to-use or when-not-to-use instructions.

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