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ex-takashima

OpenAI GPT-Image MCP Server

by ex-takashima

start_generation_job

Initiate an asynchronous background image generation job using GPT-Image. Supports multiple models, sizes, and formats; returns a job ID for status tracking and result retrieval.

Instructions

Start an async image generation job that runs in the background. Use this for long-running operations or when you want to queue multiple generations. Returns a job ID that can be used to check status and retrieve results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tool_nameYesWhich image tool to use
promptYesThe generation prompt
output_pathNoOutput file path
modelNoModel to use (default: gpt-image-1)
sizeNoImage size. gpt-image-2 also supports custom WxH (16px multiples, each edge ≤3840, ratio ≤3:1).
qualityNoImage quality
output_formatNoOutput format
sample_countNoNumber of images (1-10)
input_fidelityNoInput fidelity for edit/transform. gpt-image-1.5 only; gpt-image-2 is always high (field ignored).
Behavior3/5

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

Discloses async and background nature but does not mention side effects, rate limits, or permissions. With no annotations, description carries full burden but misses some behavioral context.

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?

Two sentences, front-loaded with purpose, no fluff. Every sentence adds value.

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?

Adequate for a 9-parameter tool with no output schema and no annotations. Covers core use, return value, and async behavior, though could mention integration with status/result tools.

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 baseline is 3. Description does not add meaningful parameter-level details beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the tool starts an async image generation job that runs in the background and returns a job ID. Differentiates from sibling tools like generate_image (sync) by emphasizing async and background execution.

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

Usage Guidelines4/5

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

Explicitly advises using this tool for long-running operations or queuing multiple generations. Implies alternatives exist (e.g., synchronous tools) but does not explicitly list when not to use.

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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