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Nexscope Ecommerce Data MCP

GPT Image 2 Generation

nexscope_gpt_image_2_generation

Generate ecommerce images with the GPT Image 2 model and optional quality control. The model is fixed by this API, so provider must not be supplied in the request.

Input Schema

NameRequiredDescriptionDefault
sizeNoCustom GPT text-to-image size, e.g. 1536x1536. Multiples of 16, at most 3840 per side, 655360..8294400 pixels, ratio at most 3:1. No reference images. Omit resolution and aspectRatio; quality defaults to medium.
promptYesGeneration prompt describing the target image, scene, composition, style, and constraints. Maximum 5000 characters.
qualityNoGPT Image quality. Supported values: low, medium, high. Defaults to medium.
imageUrlsNoOptional public reference image URLs. Omit this field for text-to-image generation, or provide references for image-to-image generation. When supplied, provide 1 to 10 JPG, PNG, or WEBP images; each image must not exceed 20 MB.
outputNumNoNumber of images to generate. Valid range: 1 to 10; defaults to 1.
resolutionNoOutput resolution. Supported values: 1K, 2K, or 4K; defaults to 1K.
aspectRatioNoOutput aspect ratio. Supported values: 1:1, 16:9, 9:16, 3:4, 4:3, 3:2, 2:3, 4:5, 5:4, 21:9, 9:21.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false and openWorldHint=true, so mutation and open-world semantics are covered structurally. The description adds a real constraint (the provider is fixed and must be omitted) and hints at 'optional quality control', but says nothing about cost, latency, or the async task/result flow implied by the sibling nexscope_media_task_result.

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

Conciseness4/5

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

Two short sentences, front-loaded with the core action, with no filler. The second sentence is slightly puzzling because no 'provider' parameter exists in the schema, which costs a bit of clarity.

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?

For a seven-parameter generation tool with no output schema and open-world side effects, the description leaves gaps: it never explains how generated images are returned, whether the call is asynchronous, or how output retrieval works. The rich schema compensates for parameter detail, but the operational picture remains incomplete.

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 all seven parameters including size, quality, imageUrls, outputNum, resolution, and aspectRatio are already documented in the schema, making 3 the baseline. The description adds only a vague 'optional quality control' reference and no syntax or routing detail beyond what the schema states.

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 ('Generate ecommerce images') and pins the model to GPT Image 2, which helps separate it from the many other image-generation siblings such as nexscope_banana_* and nexscope_seedream_5_pro_image_generation. It does not, however, explain how it differs from the near-identical nexscope_gpt_image_2_5_generation or nexscope_gpt_image_2_5_flare_generation siblings.

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 explicit when-to-use, when-not, or alternative routing. The only directive is a negative constraint ('provider must not be supplied'), and the agent is left to infer from the model name alone why it should pick this tool over the dozens of other image generators.

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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