macrosCompute protein/fat/carb grams for a calorie target using the g-per-kg-bodyweight method, with goal-based protein defaults.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | No | maintain | |
| weight | Yes | ||
| methods | No | all | |
| calories | Yes | ||
| fat_g_per_kg | No | ||
| protein_g_per_kg | No |
| Name | Required | Description | Default |
|---|---|---|---|
| results | Yes | ||
| consensus | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate a safe, idempotent read operation. The description adds useful behavioral context: it computes grams using the g-per-kg-bodyweight method and mentions goal-based protein defaults (which affect output when protein_g_per_kg is not provided). This goes beyond annotations without contradicting them.
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 concise sentence of 15 words that frontloads the essential information. Every word adds value, and there is no redundancy or fluff.
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 the presence of an output schema (not shown but indicated), the description does not need to detail return values. It sufficiently describes the tool's purpose and core inputs. The omission of the 'methods' parameter is a minor gap, but overall the description is adequate for understanding what the tool does.
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 explain parameters. It covers 'calories', 'weight', 'goal', and implied 'protein_g_per_kg' and 'fat_g_per_kg' via the bodyweight method. However, it does not mention the 'methods' parameter, which is complex (anyOf array/string with default 'all'). This leaves a notable gap for a tool with six parameters.
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 verb 'Compute', the resource 'protein/fat/carb grams for a calorie target', and the method 'g-per-kg-bodyweight with goal-based defaults'. This is specific and distinguishes from sibling tools like tdee or body-fat, which have different purposes.
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 the tool is for converting a calorie target into macronutrient grams based on bodyweight and fitness goal. It does not explicitly state when not to use it or compare to alternatives, but the context is clear enough for an agent to understand appropriate use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.