YouCam for Beauty & Personal Care

Ai Fitzpatrick Skin Type Analysis

AI-Fitzpatrick-Skin-Type-Analysis

Read-only

AI Fitzpatrick Skin Type Analysis Integrate AI driven Fitzpatrick skin type detection into your applications to classify skin types accurately using camera input. This API enables developers to build personalized skincare, sunscreen, and product recommendation workflows for eCommerce and digital health platforms. Skin Type Detection The API uses computer vision and machine learning models to analyze skin characteristics and return a Fitzpatrick classification in a single request. It provides structured, objective data that can be directly consumed by frontend applications, recommendation engines, or clinical systems. The Fitzpatrick Scale, introduced by Dr. Thomas B. Fitzpatrick, defines six skin types based on melanin levels and response to UV exposure, allowing systems to predict tendencies to burn or tan. Classification Output The API returns one of six standardized skin types from Type I to Type VI based on UV response modeling. This output enables developers to deliver tailored product recommendations, automate skincare workflows, and enhance personalization logic across user experiences while maintaining consistency and scalability.

Fitzpatrick Scale	Skin Type	Skin Reaction to Sun
Type I	White	Almost always burns, never tans
Type II	Beige	Usually burns, tans minimally
Type III	Light Brown	Sometimes burns, gradually tans
Type V	Medium Brown	Rarely burns, tans easily
Type V	Dark Brown	Very rarely burns
Type VI	Very Dark Brown	Almost never burns

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

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedAug 11, 2026

TDQS

C2.8/5.0

Behavior2/5

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

The description contains a misleading element: it says the API returns 'one of six standardized skin types', but the accompanying table lists Type V twice and omits Type IV. It also describes the operation as 'in a single request', which conflicts with the polling behavior implied by the 'polling' parameter in the schema. Annotations are not contradicted, but these errors reduce transparency.

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

Conciseness3/5

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

The description is long and has structured sections, but the inclusion of an erroneous table (duplicate Type V, missing Type IV) undermines the structure. Several sentences repeat marketing-style claims, and the description could be more concise without losing essential information.

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

Completeness2/5

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

Although an output schema exists, the description omits key operational details: how to invoke the tool with a file URL or ID, what 'index' refers to (from a detection task), and the asynchronous/polling nature. It also fails to connect this tool to its '-Detection' sibling. The description is long but still incomplete for an agent to invoke correctly in context.

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

Parameters2/5

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

With schema description coverage at 50%, the description should compensate for undocumented parameters, but it does not. It mentions 'camera input' when the schema expects 'src_file_url' or 'src_file_id', and it fails to explain the 'index' parameter or the asynchronous 'polling' option. Thus it adds little value beyond the schema and can even mislead.

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 states the tool's purpose: 'classify skin types accurately using camera input' and return a Fitzpatrick classification. It is specific about the verb and resource, but it does not explicitly differentiate from the sibling tool 'AI-Fitzpatrick-Skin-Type-Analysis-Detection', so it falls short of a full 5.

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 provides usage context by mentioning 'personalized skincare, sunscreen, and product recommendation workflows for eCommerce and digital health platforms'. However, it gives no guidance on when not to use this tool or when to choose an alternative such as the '-Detection' sibling, leaving the guidance implied rather than explicit.

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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