body_fat_calculatorBody fat percentage by the US Navy (Hodgdon–Beckett) method, metric measurements in cm. Females require hip_cm. Runs on smart-tools.xyz.
| Name | Required | Description | Default |
|---|---|---|---|
| sex | Yes | Biological sex | |
| hip_cm | No | Hip circumference in cm (required for female) | |
| locale | No | Language for the source_url link (default en) | |
| neck_cm | Yes | Neck circumference in cm | |
| waist_cm | Yes | Waist circumference in cm | |
| height_cm | Yes | Height in cm |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral burden. It discloses metric cm units, the method, and the female-specific hip requirement. However, it does not describe the return format, handling of invalid inputs, or behavior when hip_cm is missing for females, leaving some ambiguity.
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 packs essential information (method, units, and special requirement) with no filler. Every phrase earns its place.
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?
For a simple calculator, the description covers the method, units, and the key gender-specific constraint. The schema fills in parameter details. However, there is no output schema and no mention of the return format (e.g., percentage value), which prevents a perfect score.
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 100%, so parameters are already well-documented. The description adds value by clarifying units ('metric measurements in cm') and adding the conditional requirement that hip_cm is needed for females, which goes beyond the optional schema definition.
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 calculates body fat percentage using the US Navy (Hodgdon–Beckett) method, with metric measurements in cm. This specific verb+resource combination distinguishes it from sibling calculators like BMI or BMR.
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 gives clear context: it is for body fat estimation via a specific method and notes that females require hip_cm. While it doesn't explicitly name alternative tools, the context is clear enough to imply when to use it, and the female hip requirement serves as a conditional guideline.
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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Most tools are clearly distinct, but there are notable overlaps. 'hash_generator' and 'file_hash' effectively do the same thing (SHA hashing of text), and several CSV/TSV converters (csv_to_json, csv_to_sql, table_to_csv, markdown_table) have similar input handling, though outputs differ. The sheer number of converters is clear, but these near-duplicates create some ambiguity.
Names follow a generally consistent pattern: lowercase with underscores, often ending in 'converter', 'calculator', or 'generator'. A few names like 'base64', 'lorem_ipsum', and 'transliteration' deviate from the suffix pattern, but the naming style is uniform and predictable overall.
With 73 tools, this server is far over the typical well-scoped range. While the broad utility-toolkit purpose might justify a larger set, 73 is unwieldy and pushes well into 'too many' territory, making it harder for an agent to select the right tool efficiently.
The server covers a remarkably wide range of common utilities: unit conversions, calculators, text transformations, development helpers (JSON, regex, JWT, subnet), and generators. Minor gaps exist (e.g., currency converter, time zone converter), but for a general-purpose toolkit, it is largely comprehensive.