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YouCam for Fashion & Retail

Get Feature Cost

Get-Feature-Cost
Read-onlyIdempotent

Check the unit consumption for each API. The values are consistent with those listed at https://yce.perfectcorp.com/ai-api/api-pricing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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, idempotentHint=true, destructiveHint=false, so safety profile is covered. The description adds one useful behavioral detail: values are consistent with the external pricing URL, which helps set expectations for accuracy. However, it doesn't describe pagination behavior, the structure of the response, or whether any authentication is required beyond what annotations imply.

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

Conciseness5/5

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

The description is two sentences, front-loaded with the core purpose, and provides a useful reference URL. Every word earns its place with no wasted text.

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

Completeness4/5

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

Given the tool's simplicity (a read-only cost lookup with a nested request object and output schema), the description is largely complete. The URL to authoritative pricing values strengthens completeness. Missing a brief note on pagination behavior is a minor gap, but the output schema likely documents return structure. Overall, shows adequate completeness for this context.

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 0%, so the description carries no parameter details. The input schema defines 'request' as a nested object with page_size and starting_token, but the description doesn't mention pagination. With 1 parameter (request object) and no description coverage, the description adds no meaning beyond schema. However, the schema is mostly self-explanatory, and a baseline of 3 is appropriate since the agent can infer pagination from the schema.

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 checks unit consumption for each API and provides a reference URL for pricing values. It uses the specific verb 'Check' and resource 'unit consumption for each API', which makes the purpose clear. However, it doesn't explicitly distinguish it from sibling tools beyond their obvious domain differences (virtual try-on vs. cost lookup).

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 checking API pricing/consumption but doesn't explicitly state when to use it vs. alternatives or provide exclusions. The URL provides authoritative reference context, but no explicit guidance on when to invoke this tool over others.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3/5.0
Disambiguation5/5

Each AI virtual try-on tool targets a unique product category (bag, bracelet, clothes, etc.) with clear descriptions. The utility tools for upload, cost, and status are distinct in purpose despite some overlap in the upload workflow.

Naming Consistency3/5

The majority of tools follow a consistent 'AI-Product-Virtual-Try-On' pattern, but utility tools break this with mixed styles (e.g., 'File-Upload', 'Get-Upload-API-Info' vs 'upload_file'), creating inconsistency across the entire set.

Tool Count4/5

18 tools is slightly above the ideal range but justified given the diverse product categories. Each try-on tool serves a distinct need, and the utility tools are necessary for the workflow, so the count feels appropriate.

Completeness5/5

The surface covers all major fashion and retail categories for virtual try-on, includes utility tools for file upload, task status, pricing, and templates/patterns, and leaves no obvious gaps for the intended use case.

Resources