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

Annotations already declare a safe, read-only, open-world lookup, so the description's main added value is the 'Cost: 2 credits' disclosure, which is genuinely useful for planning. It omits behavioral facts an agent would want for a prediction tool, notably that the result is an estimate with confidence/accuracy limits and that the country code does not affect detection (that caveat lives only in the schema).

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 short and front-loads purpose, then use cases and cost. It loses a little to the redundant opening that repeats the title twice ('Gender Detector: Gender Detector is...'), but there is no padding beyond that.

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 prediction tool with a full output schema and complete schema descriptions, the description covers purpose, use cases, and cost. The remaining gap is qualitative: nothing tells the agent how reliable or bounded the prediction is, which matters for a probabilistic classifier.

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 schema already documents both parameters thoroughly, including the important note that country is echoed but does not change the result. The description adds only the phrase 'pass a first name', which mildly conflicts with the schema example 'John Mcdonald' (a full name). Baseline 3 is appropriate.

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 and resource: it 'estimates the likely gender of a person from their name' and returns JSON. That is clearly distinct from nearby siblings such as HumanNameParser or BabyNameGenerator. It stops short of a 5 because it doesn't define scope edges, e.g. that output is a binary classification with a probability/accuracy caveat.

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?

Usage is only implied through example scenarios ('salutations, profile defaults, or audience analysis'), which sketch intent without saying when to prefer this tool over alternatives. There are no exclusions or prerequisites, and no routing to a sibling for name parsing versus gender inference.

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