genderize_name

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

No annotations are provided, so the description carries the full behavioral burden. It does not disclose that predictions are probabilistic, that a confidence/probability value or sample count is returned, or how ambiguous or non-Western names are handled — all essential for a statistical inference tool.

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 with no wasted words. It is efficiently structured, though its brevity comes at the cost of the guidance noted above.

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 annotations and no output schema, the description is the only place to learn what comes back (probability, count, gender) and how reliable the prediction is. It leaves those gaps open for a statistical-prediction 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%, so both parameters are documented in the schema, and the baseline is 3. The description mentions only the name input and adds nothing about what the country_id localization parameter does to results.

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 ('Predict') and resource ('gender of a person') plus the input basis ('first name'), so the capability is clear. However, the sibling 'genderize_predict' appears to do the same thing, and the description makes no attempt to distinguish itself from it.

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?

No guidance on when to use this versus the near-identical sibling 'genderize_predict', nor any mention of prerequisites or limits. The agent must infer usage entirely from the tool name.

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