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submit_image_generation

Submit asynchronous GPT Image generation requests, get a task_id instantly, then query the task to auto-save completed images.

Instructions

POST generations?async=true 提交收费异步绘图,立即返回 task_id。必须继续查询才能自动落盘。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNo可选 auto 或 WIDTHxHEIGHT;两边为16倍数、最大3840、比例不超过3:1、总像素655360..8294400。省略时不发送。
groupNo分组声明:默认 reverse(逆向);仅用户明确使用官转时传 official。此字段不发送上游、不改变密钥后台分组。reverse
modelYesGPT Image 2.5 Sunburst 非常适合对编辑精度要求极高的应用场景;GPT Image 2.5 Flare 是目前用于生成高质量图片的最快模型。模型可以接受文本和图片作为输入,然后输出相应的图片。以上为用户提供的供应商选型说明,尚未做速度或质量对比实测。请结合用户意图选择并告知所用模型。
imagesNo
promptYes
qualityNo
output_dirNo可选绝对输出目录;默认配置目录。异步任务查询默认沿用提交目录。
official_optionsNo仅 group="official" 可用的官转扩展参数。未声明官转时传入此对象会在收费前拒绝。

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
costNo
noteNo
filesNo
groupNo
usageNo
statusYes
task_idNo
warningNo
progressNo
responseNo
next_toolNo
output_dirNo
fail_reasonNo
save_statusNo
wait_timed_outNo
download_errorsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A3.6/5.0
Behavior4/5

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

Annotations declare non-read-only, open-world, non-idempotent, non-destructive, but the description adds genuinely non-obvious context: the call is billed ('收费'), it returns a task_id immediately rather than the image, and persistence only happens if the agent keeps querying. This is real behavioral value beyond the structured hints.

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 compact sentences, front-loaded with the core action and its immediate result, then the follow-up obligation. Efficient, though the raw HTTP path 'POST generations?async=true' leaks implementation detail instead of describing intent.

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?

An output schema exists, so return values need not be explained, and the async/persistence workflow is covered. Still missing for an 8-parameter, paid, open-world mutation tool are the group=official vs reverse implications, the official_options precondition, and model-choice guidance.

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 63%, and the schema itself carries rich per-field documentation (size constraints, group semantics, model selection, official_options gating). The description adds no parameter-level meaning beyond the raw 'async=true' endpoint form, so it neither compensates for the coverage gap nor contradicts anything.

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 states a specific verb and resource ('提交收费异步绘图' – submit a paid asynchronous image generation) and pins down the immediate return value ('立即返回 task_id'). The 'async' qualifier implicitly separates it from the synchronous generate_image sibling, though no sibling is named explicitly.

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?

It tells the agent it must keep polling ('必须继续查询才能自动落盘'), which implies the get_image_task/wait_image_task siblings, and flags the operation as paid. However it never states when to pick this over generate_image, submit_image_edit, or submit_image_batch, leaving the selection decision to inference.

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