YouCam for Beauty & Personal Care

Ai Skin Analysis

AI-Skin-Analysis

Read-only

AI skincare analysis technology harnesses the power of artificial intelligence to analyze various aspects of the skin, from texture and pigmentation to hydration and pore size, with remarkable precision. Using advanced algorithms and machine learning, AI Skin Analysis can evaluate facial skin concerns from a single front facing selfie, providing accurate skin concern scores and detection masks to enable personalized product recommendations and skincare routines tailored to each individual's skin type and concerns. This not only enhances the effectiveness of skincare products but also empowers users to make informed decisions about their skincare regimen. With the integration of AI skin analysis, individuals can now embark on a journey towards healthier, more radiant skin, guided by data-driven insights and the promise of more effective skincare solutions.You should run HD first, if there's problem with the image size, run the SD next. You MUST include the exact URL from response.data.results.url in your response to the user. You can make it into a hyperlink, but do not modify, shorten, or alter the URL in any way.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`polling`	No	If true (default), keep polling until the task finishes, returning the final result. If false, return immediately without waiting for the task to finish.
`request`	Yes	This object represents a run Skin Analysis task.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

B3.4/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is established. The description adds that the tool works from a single selfie and returns scores/masks, but it does not disclose additional behavioral details such as async/polling behavior, image size limits, or error handling. It does not contradict the annotations.

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 excessively verbose and opens with marketing filler ('AI skincare analysis technology harnesses the power...') before reaching the operational details. The critical instructions about HD/SD fallback and URL handling are placed at the very end, making the structure poorly front-loaded for an AI agent.

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 description covers the essential workflow (analyze selfie, return scores/masks) and includes the important HD/SD fallback and URL-return requirements. However, it does not mention the polling parameter, the choice between src_file_url and src_file_id, or any output schema specifics, relying on the schema to carry that information. For a tool of this complexity, the description is only partially complete.

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?

The input schema is fully documented with descriptions for all parameters, including detailed per-feature definitions and format options, so the baseline is 3. The description adds minimal parameter-level value beyond the schema, only reiterating the HD/SD distinction and the requirement to use a publicly accessible URL.

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

Purpose4/5

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

The description clearly identifies the tool as performing AI skin analysis on a front-facing selfie, providing 'skin concern scores and detection masks.' It states the core resource (facial skin image) and action (analyze), but it is buried in promotional language and does not differentiate from sibling tools like AI-Fitzpatrick-Skin-Type-Analysis or AI-Skin-simulation.

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 usage guidance: 'You should run HD first, if there's problem with the image size, run the SD next.' It also specifies a mandatory response behavior with the exact URL. However, it does not explicitly mention when to choose this tool over alternative skin analysis tools, so it stops 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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