muscle-potentialEstimate drug-free maximum bodyweight at a target body-fat % via Casey Butt (wrist+ankle), the FFMI~25 natural cap, and Berkhan's max-contest-weight model. Men only in v1.
| Name | Required | Description | Default |
|---|---|---|---|
| sex | Yes | ||
| ankle | No | ||
| wrist | No | ||
| height | Yes | ||
| methods | No | all | |
| target_body_fat_pct | No |
| Name | Required | Description | Default |
|---|---|---|---|
| results | Yes | ||
| skipped | Yes | ||
| consensus | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds value by naming the three models used and noting 'Men only in v1', which is a behavioral constraint beyond the annotations.
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 a single sentence that efficiently fronts the purpose and key model references. It could be slightly more structured but remains concise with no redundant information.
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?
Given 6 parameters, an output schema (not shown), and multiple models, the description provides essential context but omits details like parameter dependencies (e.g., wrist/ankle only for Casey Butt) and the contradiction between 'Men only' and the schema allowing female.
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?
With 0% schema description coverage, the description must compensate. It mentions wrist+ankle and target body fat, providing context for those parameters. However, it does not explain height or sex parameters fully, and the 'Men only' note conflicts with the sex enum including 'female'.
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 clearly states the tool estimates drug-free maximum bodyweight at a target body fat percentage using three named models (Casey Butt, FFMI, Berkhan). It distinguishes from sibling tools like ffmi or tdee which focus on current metrics, not potential.
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 implies usage for drug-free potential estimation but does not explicitly state when to use this tool versus alternatives like ffmi or body-fat. No exclusion or comparison is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool targets a distinct fitness metric or calculation method, e.g., TDEE via formula vs. data, body fat via different protocols, strength, muscle mass, etc. There is no overlap that would confuse an agent.
All tool names follow a consistent lowercase-with-hyphens pattern (e.g., adaptive-tdee, one-rep-max, powerlifting-attempts). The naming clearly conveys the purpose without mixing styles.
10 tools cover a comprehensive set of fitness calculations without being excessive. Each tool earns its place, and the number is well-scoped for the domain.
The tool surface covers major fitness estimation needs: TDEE (two methods), body fat, FFMI, macros, muscle potential, strength (1RM, attempts), and sarcopenia screening. No obvious gaps for calculation-oriented fitness tools.