tdeeEstimate BMR and TDEE via Mifflin-St Jeor, Harris-Benedict, Katch-McArdle, and Cunningham. Provide body_fat or lean_mass to unlock the LBM-based methods.
| Name | Required | Description | Default |
|---|---|---|---|
| age | Yes | ||
| sex | Yes | ||
| height | Yes | ||
| weight | Yes | ||
| methods | No | all | |
| activity | Yes | ||
| body_fat | No | ||
| lean_mass | 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, idempotentHint, and destructiveHint. The description confirms it's a read estimation but adds little beyond noting the LBM method unlock. No contradictions.
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?
Two concise sentences, front-loaded with key purpose and followed by a conditional guideline. No waste.
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 complex tool with 8 parameters, nested objects, and multiple equations, the description is too brief. It doesn't explain activity levels, the methods parameter, or expected output (though 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 coverage is 0%, so the description must compensate. It only mentions body_fat and lean_mass, ignoring the other 6 parameters (sex, age, height, weight, activity, methods) which have enums and nested structures.
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 explicitly states the tool estimates BMR and TDEE via four named equations, with a clear resource (estimation methods) and verb (estimate). It distinguishes from siblings by specifying the exact equations used.
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 by mentioning 'provide body_fat or lean_mass to unlock the LBM-based methods,' but does not explicitly state when to use this tool versus alternatives like adaptive-tdee or activity-multiplier.
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.