macro_calculatorMacro Calculator: Protein, Carbs and Fat Targets — Calculate your daily protein, carb, and fat targets from your calorie goal. Choose a preset split or customize macros for your fitness or body-composition plan.
| Name | Required | Description | Default |
|---|---|---|---|
| fatPct | No | ||
| preset | Yes | ||
| carbsPct | No | ||
| proteinPct | No | ||
| dailyCalories | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral details such as return format, validation behavior, or whether custom percentages must sum to 100. It doesn't explicitly state it's a pure calculation with no side effects. The title and name already imply calculating, so the description adds minimal behavioral transparency.
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 two sentences, but the first sentence restates the title verbatim, which is redundant. The second sentence adds useful information about presets and customization. Overall, it's efficient but not maximally concise.
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?
Without annotations or an output schema, the description leaves out important context such as what the tool returns, how custom percentages are validated, and guidance on when to use it. For a 5-parameter tool, more detail is needed.
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%, and the description mentions 'calorie goal' for dailyCalories and 'preset split or customize macros' for preset, but doesn't explain the percentage parameters (fatPct, carbsPct, proteinPct) or their constraints. Parameter names are self-explanatory, providing partial compensation.
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 calculates daily protein, carb, and fat targets from a calorie goal, using a specific verb and resource. It also mentions preset splits and customization, distinguishing it from calorie/fitness calculators.
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 explicit when-to-use or alternative tools are mentioned. The phrase 'for your fitness or body-composition plan' implies a use case but doesn't differentiate from siblings like tdee_calculator or calorie_deficit_calculator.
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.
Many calculators occupy overlapping conceptual spaces, such as 'ai_roi_calculator' vs 'ai_automation_payback_calculator' and 'llm_self_host_vs_api_calculator' vs 'ai_build_vs_buy_calculator'. The boundaries between debt payoff, savings goal, and drawdown tools are also fuzzy, making it easy for an agent to select the wrong tool despite detailed descriptions.
Every tool follows the same <topic>_calculator pattern with lowercase snake_case, making the naming highly predictable and consistent. Even acronyms and numbers fit the pattern, so there is no mixing of conventions.
122 tools is an extreme number for a single MCP server, far exceeding the 50+ threshold for a severe mismatch. The tools span unrelated domains like AI costs, pet food, concrete, pizza dough, and turkey cooking, creating an unfocused kitchen-sink surface that overwhelms an agent's selection process.
The set covers many common calculator categories such as finance, construction, health, and AI costs, but several staple calculators are missing (e.g., BMI, tip, discount, simple interest, currency conversion). The AI cost cluster is over-saturated while other everyday calculations are absent, leaving minor but noticeable gaps.