calc.engineer

bmi calculator

bmi_calculator

Calculate Body Mass Index (BMI) from weight and height using the WHO standard formula. Supports metric (kg/cm) and imperial (lbs/inches) units. Returns the BMI value, WHO classification (Underweight, Normal, Overweight, Obese Class I-III), and the healthy weight range for the given height. Formula: BMI = weight_kg / (height_m)^2. Useful for health screening, fitness planning, and clinical intake forms.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`unit`	No	Unit system. 'metric' = kg and cm. 'imperial' = lbs and inches. Defaults to 'metric'.	metric
`height`	Yes	Height. Units determined by the 'unit' parameter.
`weight`	Yes	Body weight. Units determined by the 'unit' parameter.

Output Schema

TableJSON Schema

Name	Required	Description
`bmi`	Yes	Body Mass Index value rounded to 1 decimal.
`category`	Yes	WHO BMI classification: Underweight, Normal weight, Overweight, Obese Class I/II/III.
`weight_unit`	Yes	Unit of the weight values in this response (kg or lbs).
`healthy_weight_range_low`	Yes	Low end of healthy weight range (BMI 18.5) in the input unit system.
`healthy_weight_range_high`	Yes	High end of healthy weight range (BMI 24.9) in the input unit system.

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, but the description fully explains the behavior: it is a read-only computation with no side effects. It specifies formula, unit handling, and output data. No destructive or auth concerns arise.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, concise and well-structured. It front-loads the core purpose and adds helpful detail without verbosity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity and the presence of an output schema (context signal), the description covers all necessary information: inputs, units, formula, outputs, and use cases. It is fully adequate for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds context about units and formula but does not provide significant additional semantics beyond the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states it calculates BMI using WHO standard formula, supports metric/imperial units, and lists outputs (BMI value, classification, healthy weight range). This clearly distinguishes it from sibling tools, which are all other calculators.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions use cases: health screening, fitness planning, clinical intake forms. While it doesn't explicitly state when not to use or alternatives, the sibling list contains no other BMI calculator, so context is sufficient.

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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TDQS

A3.9/5.0

Disambiguation4/5

Despite 89 tools, each has a clearly distinct purpose with detailed descriptions that often reference related tools. Overlap exists (e.g., multiple LoRa/RF tools), but the descriptions are sufficient to distinguish them. Some confusion possible among similar-sounding tools like attenuator_pi and attenuator_tee, but the descriptions explicitly compare them.

Naming Consistency4/5

Consistent underscore-separated lowercase naming. Most tools follow a verb_noun pattern (e.g., capacitor_charge, wire_gauge) or noun_noun (power_cost). Minor inconsistencies such as 'bmi_calculator' vs 'solar_sizing' but overall predictable.

Tool Count2/5

89 tools is far too many for a single MCP server. This scope is more appropriate for multiple specialized servers. The sheer number will slow agent selection and increase cognitive load, reducing coherence.

Completeness3/5

Covers many domains (RF, solar, PCB, networking, math, etc.) but lacks depth in some areas (e.g., no three-phase power, no airflow calculations). Some domains have comprehensive coverage (LoRa/Meshtastic), but others feel incomplete for the tool count.

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