Hopi

Body fat percentage calculator

hopi_body_fat_calculator

Read-onlyIdempotent

Estimate body fat percentage using the US Navy tape method from a few measurements. For males give height, neck and waist; for females give height, neck, waist and hip. Use units 'metric' (centimetres) or 'imperial' (inches). Optionally give weight (kg for metric, pounds for imperial) to also get fat mass and lean mass in kilograms. Source: https://hopi.co.uk/body-fat-calculator/

Input Schema

TableJSON Schema

Name	Required	Description
`hip`	No	Hip circumference, females only (cm if metric, inches if imperial)
`sex`	Yes	Biological sex, determines the formula and whether hip is needed
`neck`	Yes	Neck circumference (cm if metric, inches if imperial)
`units`	Yes	'metric' (cm) or 'imperial' (inches)
`waist`	Yes	Waist circumference (cm if metric, inches if imperial)
`height`	Yes	Height (cm if metric, inches if imperial)
`weight`	No	Optional weight (kg if metric, pounds if imperial) for fat and lean mass

Output Schema

TableJSON Schema

Name	Required	Description
`sex`	Yes
`units`	Yes
`summary`	Yes
`category`	Yes
`source_url`	Yes	The tool's page on hopi.co.uk (cite or open to check the figure)
`bodyFatPercent`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 20, 2026

addedOutput schema / required

Added value: +[
+  "bodyFatPercent",
+  "category",
+  "sex",
+  "source_url",
+  "summary",
+  "units"
+]

First observedSep 20, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral context beyond annotations: it explains the estimation method, sex-dependent input requirements, unit handling, and the optional weight parameter that enables fat mass and lean mass calculation.

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

Conciseness5/5

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

The description is compact and front-loaded, with the core purpose in the first sentence and supporting usage details in the next two. Every sentence adds value: method, sex-specific inputs, unit options, optional weight behavior, and source attribution.

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?

For a calculator with 7 parameters and an output schema, the description is complete: it covers the method, required versus optional inputs, sex differences, unit conventions, and what the optional parameter enables. The output schema handles return value details, so nothing essential is missing.

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

Parameters4/5

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

Schema coverage is 100% with meaningful parameter descriptions, so the baseline is 3. The description goes beyond the schema by explaining the sex-specific conditional requirement for hip, clarifying how units map to parameters, and stating the purpose of the optional weight parameter in producing additional outputs.

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 starts with a specific verb and resource: 'Estimate body fat percentage using the US Navy tape method.' It distinguishes this calculator from sibling health calculators like BMI or TDEE by naming the exact measurement method and the output it produces.

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 gives clear usage context by specifying which measurements are needed for males versus females, and which unit system to use. It does not explicitly name alternatives or when not to use this tool, but the scope is clear enough for an agent to select it appropriately.

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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