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Glama

YouCam for Beauty & Personal Care

Ai Hair Volume Virtual Try On

AI-Hair-Volume-Virtual-Try-On

Enhance Your Look with Fuller, More Voluminous Hair Instantly!​ Add natural volume to fine or thinning hair. Seamlessly fill gaps or add hair with AI. Works for all hair types: straight, curly, thin. Perfect for dating profiles, resumes & more. Our AI tool helps you achieve perfect hair volume and density in all your photos, whether for personal, professional, or social use. Say goodbye to bad hair days in pictures and hello to fresh, voluminous hair every time. Use case: Suggestions for How to Shoot:

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

B3/5.0
Behavior2/5

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

Annotations indicate the tool is not read-only, not idempotent, and not destructive, but the description adds little about what actually happens during processing. It mentions 'add natural volume' but does not disclose side effects, input requirements (e.g., face photo), output format, or any operational caveats. The marketing language ('Say goodbye to bad hair days') provides no behavioral value.

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 verbose, repetitive (mentions volume multiple times), and ends with an incomplete 'Use case: Suggestions for How to Shoot:' that appears truncated. Marketing fluff like 'Say goodbye to bad hair days' adds no informational value. A more concise, structured description would be more effective.

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?

Given the tool's complexity (nested request object, polling, output schema, and many sibling tools), the description is incomplete. It fails to mention the need to list templates first, how to upload files, or what the result looks like. The existence of an output schema lowers the burden, but the description still omits critical workflow details and leaves a dangling 'Use case' section.

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?

Schema description coverage is 50%, and the description provides no parameter-level information. The schema documents 'polling' and 'request' with nested fields, but the description does not clarify how to use them, what template_id means, or how to obtain src_file_url/src_file_id. The description's vague mention of 'Suggestions for How to Shoot' does not compensate for the lack of parameter guidance.

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 states the tool's function: adding natural volume to fine or thinning hair and filling gaps with AI. It distinguishes itself from sibling tools like hair extension or color by focusing specifically on volume. The verb+resource is specific and observable.

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 implies usage for dating profiles, resumes, and social photos, giving some context. However, it does not explicitly state when to choose this tool over alternatives like AI-Hair-Extension-Virtual-Try-On, nor does it provide exclusions. The abrupt 'Use case: Suggestions for How to Shoot:' with no content weakens guidance.

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