YouCam for Beauty & Personal Care

Ai Makeup Transfer

AI-Makeup-Transfer

Just Upload a Desired Photo with the Look You Like! AI Makeup Transfer makes it easy and fun to experiment with different looks by letting you to upload desired photo to try them one by one. Have any makeup look you want to try now? Let us amaze you with AI Makeup Transfer! First, upload a photo of yourself where your face and its features are clearly visible as the target image. Then, upload a photo of your favorite makeup look as the reference image. There you have it - an AI Makeup Transferred photo. Samples:

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

TDQS

B3.1/5.0

Behavior2/5

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

The description adds only an input quality requirement (clear face visibility) and the basic upload/reference workflow. It does not disclose processing behavior, side effects, wait times, or how files are handled, which matters given annotations indicate the tool is not read-only and may have open-world effects. No contradiction with annotations, but little extra context.

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 overly promotional and verbose, with repetitive phrasing ('upload... upload...'), exclamation points, and a dangling 'Samples:' that suggests incomplete content. It lacks a crisp, front-loaded summary and would be better as a single direct sentence.

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?

Given the tool's simplicity and the presence of an output schema, the description covers the core concept and input roles. It does not mention the polling parameter or file ID vs URL options, but these are documented in the schema. Overall adequate but not comprehensive for an agent deciding how to invoke it.

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?

The description maps 'src' to the target photo ('upload a photo of yourself') and 'ref' to the makeup reference ('upload a photo of your favorite makeup look'). This semantic distinction is absent from the schema's generic 'Url of the file to run task' descriptions, adding significant meaning beyond the structured fields.

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 conveys that the tool transfers a makeup look from a reference photo to a target photo (the user's own photo). It describes the workflow of uploading two photos, which distinguishes it from single-photo virtual try-on siblings, even though it doesn't name them explicitly.

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 procedural instructions with prerequisites ('upload a photo of yourself where your face and its features are clearly visible'), implying the tool is for replicating a specific makeup look. However, it does not explicitly state when to use this tool over alternatives like AI-Makeup-Virtual-Try-On, nor does it mention when not to use it.

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.

Resources

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