Zelta Waist Size Check

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

Annotations already establish readOnlyHint, idempotentHint, destructiveHint=false and closed-world scope, so the safety profile is covered. The description adds real scope boundaries beyond the annotations by excluding children, pregnancy, and medical/treatment questions, which materially changes when the agent should route away. It does not describe what the assessment output looks like.

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?

Front-loaded with the usage trigger, followed immediately by the exclusion clause, so the routing logic comes first. The example list is longer than strictly necessary but each example demonstrates a distinct unit or ratio case, so most of the length 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 6-parameter, single-required-parameter calculation tool with full schema coverage and no output schema, the description supplies the scope, trigger conditions, and exclusions an agent needs. The one omission is that it never characterizes the returned comparison result, which an agent might want since no output schema exists.

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%, with units, ranges, and measurement guidance (belly button, after breathing out) already documented per parameter, so the baseline is 3. The example queries hint at unit combinations (inches vs cm, waist-to-height pairs) but add no syntax or constraint detail beyond what the schema states.

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 action on a specific resource: comparing an adult's waist measurement against a suggested fitness range. It is clear what the tool computes, though it never names the sibling waist_target, so disambiguation from that tool relies on the agent inferring that 'target' means setting a goal rather than checking a measurement.

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?

Strong when-to-use signal with four concrete example phrasings ('is 36 inch waist too big for a man', 'waist to height ratio 92 cm 170 cm'), plus explicit exclusions for children, pregnancy, and medical/treatment/medication questions. The only gap is that the sibling waist_target is never named as the alternative for goal-setting requests.

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

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

Annotations already establish read-only, idempotent, non-destructive, closed-world behavior. The description adds useful substantive context by saying what it returns (half their height and the suggested Indian range limit) and by excluding inappropriate populations.

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?

It is front-loaded with the use case, then examples, then return content, then exclusions. Every sentence serves a purpose and there is no filler.

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 four-parameter read-only calculator with no output schema, the description provides enough scope, output expectation, and exclusions for correct selection. It could be slightly richer on the feet/inches versus centimetres input path, but the schema handles that.

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 parameters are already documented in the schema, including the sex enum and height units. The description reinforces height-based calculation but adds no syntax or unit-selection guidance beyond the schema.

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 resource and output: waist-size target from height, returning half-height and a suggested Indian range limit. It does not distinguish this from the sibling waist_check, so it stops short of a 5.

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?

It gives clear when-to-use examples and explicit when-not-to-use exclusions for children, pregnancy, and medical questions. It does not name the sibling waist_check as an alternative, so it is not fully complete on alternatives.

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