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

readOnlyHint=true already establishes this as a safe, non-mutating calculation, so the safety burden is covered. The description adds nothing about the underlying formula (e.g., whether bodyFatPct switches to Katch-McArdle), how defaults are applied, or that results are derived rather than stored.

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 tight sentence that front-loads the computation and lists the outputs in order of derivation. No filler, though it is arguably too terse given the undocumented parameters.

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?

With no output schema, the description does at least name what is returned, which is the minimum. But for a 7-parameter calculation with 0% schema coverage, it omits how the optional activity, goal, and bodyFatPct inputs shape the result.

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 0% across 7 parameters, so the description carries the full explanatory burden and provides none of it. It never mentions gender, activity level, goal, or bodyFatPct, nor how activity/goal modify the recommended-calorie output.

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 (compute) and the concrete outputs (BMR, TDEE, recommended daily calories) plus the input source (client stats). It is distinguishable from calc_1rm and calc_macros by output, though it never names those siblings explicitly.

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?

No when-to-use guidance, no prerequisites, and no indication of when to pick this over calc_macros (which likely consumes the same calorie outputs). The agent must infer the workflow placement itself.

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