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YouCam for Beauty & Personal Care

Ai Hair Frizziness Detection

AI-Hair-Frizziness-Detection
Read-only

180° Full View Hair Frizz Analysis with Just 3 Photos Our AI Frizzy Hair Analyzer delivers precise hair frizz analysis in seconds by simply uploading 3 photos—front, left, and right views of the hair. This efficient process delivers accurate results in seconds, enabling businesses to offer tailored hair solutions and defrizz hair products based on hair frizz levels, without the need for time-consuming in-person consultations, complicated hair quizzes, or specialized hardware installations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pollingNoIf true (default), keep polling until the task finishes, returning the final result. If false, return immediately without waiting for the task to finish.
requestYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds that exactly 3 photos (front, left, right) are required and that analysis is fast, but this is mostly redundant with the input schema. It does not disclose potential error conditions, rate limits, or polling behavior beyond what the schema already shows.

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

Conciseness2/5

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

The description is repetitive ('in seconds' appears twice, 'efficient' appears once) and includes marketing fluff about enabling businesses and avoiding consultations/hardware. The second paragraph adds little actionable information and should be condensed or removed for API consumers.

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?

The tool has a simple schema and output schema, so the description doesn't need to explain return values. However, it lacks guidance on how to feed the 3-photo requirement into the request (beyond schema examples) and doesn't mention the file upload prerequisite. It is adequate but not complete for an agent unfamiliar with the API.

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 50%; the request parameter lacks a top-level description, but its sub-properties (src_file_urls/src_file_ids) are described. The description clarifies the need for three views front/left/right, which helps map to the array, but it does not explain the polling parameter or how to obtain file URLs/IDs, leaving some gaps.

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 identifies the tool as an AI hair frizz analyzer that performs precise frizz analysis using 3 photos. It distinguishes itself from sibling detection tools (e.g., AI-Hair-Density-Detection) by specifying the unique 3-photo front/left/right input and the 180° full-view approach.

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 provides clear context for when to use this tool: when hair frizziness needs to be assessed for tailored product recommendations. It implies the tool is for analysis rather than virtual try-on, but does not explicitly name alternatives or specify when not to use it, so it falls short of a 5.

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

C2.7/5.0
Disambiguation3/5

Most tools target distinct features (abs, aging, bangs, beard, etc.), but there are several overlapping pairs like AI-Face-Lift vs AI-Face-Reshape and AI-Skin-Analysis vs AI-Skin-simulation that could confuse an agent. The '-Detection' and '-Templates' suffixes help, but the sheer number of similar virtual try-on tools makes selection less obvious.

Naming Consistency2/5

The majority of tools follow an 'AI-<Feature>-<Action>' pattern with hyphens, but there are clear inconsistencies: 'Get-Feature-Cost', 'Get-Running-Task-Status', and 'File-Upload' break the pattern, and 'upload_file' uses snake_case. The mixture of 'Filter', 'Simulator', 'Generator', 'Virtual-Try-On', and 'Detection' suffixes also lacks a standardized verb/noun structure.

Tool Count2/5

With 46 tools, the server exceeds the 25-tool threshold and feels bloated. Many 'Templates' tools (e.g., AI-Bangs-Filter-Virtual-Try-On-Templates) and separate detection/action pairs add redundancy. The scope is broad but could be consolidated into fewer, more generic tools.

Completeness4/5

The server covers a wide range of beauty and personal care features: hair, face, skin, body, makeup, and nails, including both try-on and detection/analysis capabilities. Minor gaps exist (e.g., no eyelash try-on), but the surface is quite comprehensive for the stated purpose, with no major dead ends.

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