calories_burned_calculatorCalories Burned Calculator (MET-Based) — Calculate calories burned by activity with the MET formula: enter weight, minutes, and activity to estimate energy spent, and compare exercises side by side.
| Name | Required | Description | Default |
|---|---|---|---|
| met | Yes | ||
| minutes | Yes | ||
| weightKg | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral disclosure burden. It discloses that it uses the MET formula and supports side-by-side comparison, but it doesn't describe output format, rounding, or units. For a simple calculator this is adequate though not rich.
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, front-loaded sentence with no fluff. Every phrase adds value, including the formula and the side-by-side comparison feature.
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?
The description is complete enough for a simple calculator with three primitive parameters and no output schema. It explains the conceptual inputs and what is estimated, but it doesn't describe the output format or example values, which would help an agent invoke it correctly.
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 mentions 'weight, minutes, and activity' but doesn't explicitly map to the actual parameters (met, weightKg, minutes), using 'activity' for the 'met' field. This adds partial meaning but could mislead and leaves the units and MET interpretation unclear.
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 a specific verb ('calculate') and resource ('calories burned by activity'), with a distinctive method (MET formula) and an additional capability (compare exercises side by side). It effectively differentiates from sibling calorie-based calculators like calorie_deficit_calculator.
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 usage context is implied but not explicit: it says to use this calculator to estimate energy spent with MET, but it doesn't mention when to prefer this over alternatives like tdee_calculator or macro_calculator. No exclusions or alternative references are provided.
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.