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codex-openai-image-mcp

codex-openai-image-mcp

by meet30997

Generate image

generate_image

Creates one to four PNG images from a text prompt, saves full-size files with prompt and settings metadata, and returns file paths plus small previews for review.

Instructions

Generate one or more images from a text prompt. Saves full-size PNGs (plus a .json sidecar with the prompt and settings) in the output folder and returns their paths with small previews.

Input Schema

NameRequiredDescriptionDefault
sizeNoWidth x height in px, e.g. "1024x1024" or "1536x1024".1024x1024
countNoNumber of variants to generate (1-4).
promptYesWhat to draw. Be specific about subject, style, composition and colours.
out_nameNoFile name without extension, relative to the output folder, e.g. "icons/rocket". Defaults to a slug of the prompt.
referencesNoPaths of existing images to match the style of (inside the working folder).
transparentNoAsk for a transparent background.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already flag a non-read-only, open-world, non-destructive operation. The description adds real value beyond them: it discloses that full-size PNGs plus a .json sidecar are written to the output folder and that paths with small previews are returned. It stops short of covering overwrite behavior when out_name collides, cost/latency of the external call, or failure modes.

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, zero filler, and the core action is front-loaded before the side effects. Every clause adds information an agent needs.

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?

For a six-parameter mutation tool with no output schema, the description adequately covers what is produced and where, so the agent knows the return shape. Remaining gaps are overwrite semantics for out_name and any cost/rate-limit caveats for the external generation call.

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 six parameters are already documented with defaults and ranges. The description adds only output-side context ('one or more', paths, sidecar) and no syntax or semantics beyond the schema, which matches the baseline of 3 when the schema carries the burden.

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 gives a specific verb (generate) and resource (images) with the source constraint 'from a text prompt', which implicitly separates it from edit_image. It never names or contrasts the siblings edit_image or generate_set, so the boundary with generate_set ('one or more images') is left ambiguous.

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?

Usage is implied by 'from a text prompt' and the file-output behavior, but there is no explicit when-to-use, when-not-to-use, or routing to edit_image/generate_set. An agent must infer that this is the text-to-image entry point rather than a set generator or editor.

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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