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, yet it discloses nothing about the operation's nature. It does not state that this is a pure, side-effect-free computation, nor what the result represents (raw BMI number, rounded value, or a category label such as underweight/obese).

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 filler or redundancy, appropriately sized for a trivial two-parameter tool. It is terse to the point of omitting units, though that information lives in the schema.

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 simple deterministic calculator with fully documented parameters this is close to adequate. The remaining gap is the unspecified return value—BMI number versus interpreted category—which matters because there is no output schema to answer that question.

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 coverage is 100% and the schema already documents units for both parameters ('Height in cm', 'Weight in kg'). The description names weight and height but adds no unit, range, or format detail beyond what the schema provides, so the baseline 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?

Names a specific verb ('Calculate') and resource ('BMI') and identifies the two inputs, so the operation is unambiguous. It does not distinguish itself from nearby computational siblings like bmr_calculator or calorie_burn, leaving an agent to infer from the name alone which health metric applies.

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 gives no when-to-use context, no prerequisites, and no pointer to alternatives such as bmr_calculator for metabolic rate or calorie_burn for activity expenditure. An agent must guess which calculator fits a health-related query.

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