API Hub MCP — 337+ Tools

body_fat_calc

Estimate body fat percentage using BMI or Navy method with neck, waist, and hip measurements.

Instructions

Estimate body fat percentage using measurements or BMI-based formula.

Parameters:
    weight — Weight in kg.
    height — Height in cm.
    age — Age in years.
    sex — 'male' (default) or 'female'.
    neck — Neck circumference in cm (optional, for Navy method).
    waist — Waist circumference in cm (optional, for Navy method).
    hip — Hip circumference in cm (optional, for Navy method, female).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "weight": "<weight>",
      "height": "<height>",
      "age": "<age>"
    },
    "name": "body_fat_calc"
  }
}

Input Schema

Name	Required	Default
`weight`	Yes
`height`	Yes
`age`	Yes
`sex`	No	male
`neck`	No
`waist`	No
`hip`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.4.1Jun 22, 2026

TDQS

A3.8/5.0

Behavior3/5

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

No annotations provided; the description carries full burden. It explains the estimation methods and parameter roles but lacks details on accuracy, limitations (e.g., Navy method for adults only), or that results are estimates. Some transparency, but not comprehensive.

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 concise: a lead sentence followed by a parameter list with brief explanations. No wasted words, but the list could be formatted more cleanly (e.g., bullet points or table). Still efficient and front-loaded.

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

Completeness3/5

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

With 7 parameters (3 required, 4 optional) and an output schema present, the description covers the tool's purpose and input semantics. However, it omits context about formula sources or output interpretation (e.g., body fat category). Adequate but not fully complete.

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 0%, so description compensates well. All parameters are listed with units (e.g., weight in kg) and optionality clarified (e.g., 'optional, for Navy method'). This adds meaning beyond the schema's titles, though default values could be explicitly noted.

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 clearly states the tool estimates body fat percentage using either measurements or a BMI-based formula. It distinguishes between two methods and names specific parameters for the Navy method. This contrasts with sibling tools like bmi_calc (which calculates BMI, not body fat).

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

Usage Guidelines3/5

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

The description implies use cases through parameter lists (optional measurements for Navy method) but does not explicitly state when to use this tool versus alternatives like bmi_calc or tdee_calc. No exclusion criteria or prerequisites are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server