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Nexscope Ecommerce Data MCP

AI Apparel Image Set & Photography API v3

nexscope_ai_apparel_image_set_photography_v3

Create apparel ecommerce image sets from clothing images with optional model and scene references.

Input Schema

NameRequiredDescriptionDefault
noTextNoWhether generated images should avoid text.
qualityNoGPT Image quality only. Supported values: low, medium, or high.
aPlusNumNoNumber of standard A+ images, from 0 to 12. Defaults to 0.
brandKeyNoOptional brand information serialized as a JSON string. Supported keys: brandColor, fontStyle, salesRegion, language, platform, customSettings.
providerNoImage model. Defaults to BANANA_2. Supported values: BANANA_2, BANANA_2_LITE, BANANA_PRO, WAN2_7, SEEDREAM5_PRO, GPT_2_IMAGE.
imageListYesPublic apparel image URLs. Provide no more than 5 JPG, JPEG, PNG, or WEBP images; each image must be at most 10 MB and between 384 and 4096 pixels.
insTypeNumNoNumber of social-style images, from 0 to 12. Defaults to 0.
resolutionNoTarget resolution supported by the selected provider.
aPlusProNumNoNumber of premium A+ images, from 0 to 12. Defaults to 0.
aPlusStylesNoOptional A+ style entries. Each item may contain a saved style id or a custom prompt.
aspectRatioNoTarget aspect ratio.
pointStylesNoOptional selling-point style entries. Each item may contain a saved style id or a custom prompt.
sellerPointNoProduct selling points, up to 2500 characters. Required when isExtractPrePoint is false.
sizeTypeNumNoNumber of size-information images, from 0 to 12. Defaults to 0.
isExactSceneNoWhether to match the provided scene closely. Defaults to true.
modelTypeNumNoNumber of model images, from 0 to 12. Defaults to 0.
whiteTypeNumNoNumber of white-background images, from 0 to 12. Defaults to 0.
aPlusHasPhoneNoWhether A+ output includes phone display layouts.
modelImageUrlNoOptional model reference image URL.
sceneImageUrlNoOptional scene reference image URL.
sellerTypeNumNoNumber of selling-point images, from 0 to 12. Defaults to 0.
aPlusAspectRatioNoA+ output aspect ratio. Supported values: 16:9, 9:16, 1:1, 1:3.7.
replaceLibraryIdNoOptional replacement-word library ID used to rewrite generated copy.
isExtractPrePointNoWhether to automatically extract selling points. Defaults to false. When false, sellerPoint is required.
modelAndSceneInfoNoModel and scene information serialized as a JSON string or concise structured text.
sensitiveLibraryIdNoOptional sensitive-word library ID used to screen generated copy.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
Behavior3/5

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

The description is consistent with annotations (readOnlyHint=false, openWorldHint=true) — "create" matches a non-read-only, open-world generation operation. However, beyond that it discloses nothing about the long-running/async nature, cost, or that results likely need polling via nexscope_media_task_result; with 26 params and no output schema, that is a notable gap.

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 waste, which is good structurally, but for a 26-parameter multi-mode generation tool it is arguably too terse to be "appropriately sized" — the brevity comes at the cost of guidance rather than surplus.

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 26 parameters, multiple generation modes (A+, selling-point, size, model, white-background counts) and no output schema, the one-sentence description is far from complete enough. It omits how modes combine, whether counts can be zero, and how output is retrieved, which an agent needs before invoking.

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 coverage is 100%, so every one of the 26 parameters is already documented in the schema, and the description adds only the concept of optional model/scene references. Baseline 3 is appropriate since the schema does the heavy lifting.

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 and resource: "Create apparel ecommerce image sets from clothing images," plus the optional model/scene reference inputs. It does not name or differentiate against near siblings such as nexscope_ai_product_marketing_image_set_v4 or nexscope_product_scene_variations, so an agent must infer the split from names alone.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no statement of when to use this tool versus the many image-set/background/model siblings, and no prerequisites or exclusions. The only usage signal is the implied "apparel input required" from the description.

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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