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

Annotations already declare this as a safe, idempotent, closed-world read, and an output schema documents the return values, so the description's return description is largely redundant. The added formula (VMA × 3.5) is genuine extra context, but behavioral disclosure beyond structured fields remains modest.

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?

Four short sentences, each earning its place: what it does, who uses it, the formula, input/output, and a navigation hint. Front-loaded with the core purpose and free of 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 single-parameter, read-only computation with a full output schema and complete annotations, the description supplies everything an agent needs: the formula, the input unit, the output units, and a bundling referral.

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%, so the baseline is 3; the description adds value by expanding the acronym VMA to "maximal aerobic speed," which the schema does not define. It otherwise repeats the unit already present in 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?

States a specific verb and resource ("Estimate VO2max from VMA") and identifies the audience (runners assessing cardio fitness). An agent can distinguish it from the inverse calculate_vma and other fitness tools without opening the schema.

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?

Gives clear usage context ("Use for runners assessing cardio fitness") and points to list_bundles for related 'sport' calculators. It lacks explicit when-not guidance or a named alternative tool, so it stops short of the 5 tier.

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