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Nexscope Ecommerce Data MCP

AI Hand Repair API

nexscope_ai_hand_repair

Improve hand appearance in generated model or lifestyle images.

Input Schema

NameRequiredDescriptionDefault
maskUrlNoOptional mask URL for the hand repair area.
imageUrlYesPublic URL of the image requiring hand repair. The image must be between 300 and 4096 pixels in both width and height, and its width-to-height ratio must be between 0.4 and 2.5.
outputNumNoNumber of images to generate, from 1 to 4. Defaults to 1.
personTypeYesPerson type. Supported values: 0 (girl), 1 (boy), 2 (adult woman), 3 (adult man).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3/5.0
Behavior2/5

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

Annotations only declare readOnlyHint=false and openWorldHint=true. The description adds nothing about whether this is synchronous or async, whether the original image is modified or a new one returned, cost/rate limits, or that outputNum controls how many result images come back. For an open-world generation call that is a real gap. No contradiction with readOnlyHint=false.

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?

A single front-loaded sentence with no filler, but it is terse to the point of under-specification rather than efficiently complete; nothing is wasted but little is delivered.

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?

With four parameters, no output schema, and a generative open-world operation, the description should say at minimum what comes back (image URLs?) and roughly how long or in what form. Nothing about returns or asynchronous behavior is stated, leaving the agent to guess how to consume results.

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 100%, so the schema already explains imageUrl constraints, the maskUrl option, personType values, and outputNum range. The description contributes no additional parameter meaning, which meets the baseline of 3 for fully documented schemas.

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?

States a specific verb+resource: improving hand appearance in generated model or lifestyle images. That is clearly distinguishable from generic siblings like nexscope_image_inpainting or nexscope_smart_image_edit_manipulation_v3, though the description never names them or explains the boundary.

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 phrase 'generated model or lifestyle images' implies the use case (fixing hands in AI-generated model photos) but gives no explicit when-to-use rules, no exclusions, and no pointer to alternative edit tools when the problem isn't a hand.

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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