heart_rate_zone_calculatorHeart Rate Zone Calculator — Calculate your five heart rate training zones from your age using the 220 − age or Tanaka formula, with an optional Karvonen resting heart rate refinement.
| Name | Required | Description | Default |
|---|---|---|---|
| age | Yes | ||
| formula | Yes | ||
| restingHR | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the formula options (220−age or Tanaka) and the optional Karvonen refinement, which goes beyond the schema's enum labels. Since it's a calculator, no destructive behavior is expected, and the description is honest about its capabilities.
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 that immediately states the action and resource. Every part contributes value without redundancy or filler.
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 communicates the core purpose and inputs, but it does not specify the output format (e.g., zones as BPM ranges or percentages). Since there is no output schema, a bit more detail on return values would be helpful, but the mention of 'five heart rate training zones' provides a reasonable expectation.
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%, but the description effectively covers all three parameters: age ('from your age'), formula ('220 − age or Tanaka formula'), and restingHR ('optional Karvonen resting heart rate refinement'). This adds meaningful context beyond the raw schema.
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's function: calculating five heart rate training zones from age using specified formulas. It distinguishes itself from the many sibling calculators by naming its specific domain and key variables.
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 provides clear context for when to use this tool—when heart rate zones are needed—but does not explicitly mention exclusions or alternative tools. Given the self-explanatory nature of a calculator among a list of distinct calculators, this is adequate.
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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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.