Gender from a name

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

With no annotations provided, the description carries the full burden and does well: it discloses that the response includes probability, sample size, and match type, and that uncertain names return 'unknown' rather than a guess. It stops short of specifying the exact response shape or error behavior, but it is notably transparent for a read-only prediction 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?

The description is three tight sentences with the purpose front-loaded. The follow-up details about response fields and the 'unknown' fallback are valuable and not redundant; every sentence 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?

For a two-parameter read tool with no output schema, the description covers the purpose, the key response fields, and the uncertainty behavior. It could strengthen sibling differentiation, but nothing essential appears missing for an agent to call it correctly.

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 the input schema already documents both the name and country parameters, including country-specific weighting. The description's mention of 'a person's name' adds no new parameter semantics beyond what the schema provides, so the baseline score of 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 opens with a specific verb and object: 'Predicts gender from a person's name.' This clearly distinguishes the tool from siblings like gender_from_email and gender_from_username, and the singular framing separates it from gender_bulk. The core function is 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?

The description implies the tool is for name-based input, which differentiates it from email- and username-based siblings, but it never explicitly states when to choose this over alternatives. There are no conditions, exclusions, or routing guidance, so usage context is only implied rather than stated.

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