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eduard256

mcp-openai-images-audio

by eduard256

image

Create or modify images by describing them in text, optionally using reference images. Automatically selects the best OpenAI model and saves the output to a file path.

Instructions

Generate, edit, or compose images via OpenAI's gpt-image family.

BEFORE the FIRST call in a conversation, read the MCP resource image-guide://full for the full prompting guide (structure, realism rules, edit/compose modes, when to set quality/fidelity). You only need to read it once per conversation.

Mode is selected by references_paths:

  • omitted/empty -> generate from text alone

  • 1 path -> edit that image

  • 2..16 paths -> generate using them as labeled references

Model routing is automatic and reported in the response:

  • background='transparent' -> gpt-image-1.5 (gpt-image-2 rejects alpha)

  • everything else -> gpt-image-2 (flagship)

Returns metadata only — the file is written to output_path. Read the file with the Read tool only if you need to verify the result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesStructured English description of the desired image. When references_paths has more than one entry, label each one explicitly in the prompt (e.g. "Image 1: subject. Image 2: style reference."). See image-guide://full for the full prompting guide.
output_pathYesABSOLUTE filesystem path where the result will be saved. Extension determines format: .png / .jpg / .jpeg / .webp. Parent directory MUST already exist (create it via Bash mkdir -p before retrying). File MUST NOT already exist.
sizeYesOutput resolution. REQUIRED — pick deliberately based on the use case: - 1024x1024 — generic single subject, avatar, icon - 1536x1024 / 1024x1536 — landscape / portrait composition - 2048x2048 — high-res square (hero blocks, album art) - 2048x1152 / 1152x2048 — 16:9 / 9:16 banners, video thumbs - 3840x2160 / 2160x3840 — 4K, only when text/UI must be crisp
references_pathsNoOptional. Up to 16 ABSOLUTE paths to existing PNG/JPG/WebP files (each ≤50 MB) used as input images. Omit for pure text-to-image generation.
qualityNoOptional. OMIT in most cases — the default ('auto') already produces excellent quality. Pass 'low' for cheap drafts. Pass 'high' only when text legibility (UI mockups), photorealism, or final-output quality is critical.
input_fidelityNoOptional. Only relevant when references_paths is set. Pass 'high' when faces/identity must be preserved exactly (portrait edits, virtual try-on, product placement). Otherwise omit — defaults to 'low' on the OpenAI side, which is cheaper and faster.
backgroundNoBackground handling. Use 'transparent' for logos, icons, isolated products, or anything you'll composite later (only valid with .png/.webp). 'opaque' forces a solid background. 'auto' lets the model decide.auto

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations exist, so the description fully bears the burden of behavioral disclosure. It explains mode selection, automatic model routing, output behavior (returns metadata, writes to file), and file constraints (absolute path, parent directory must exist, file must not already exist). All behavioral traits are disclosed.

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?

The description is well-structured with clear sections for first-time setup, mode selection, model routing, and output behavior. Every sentence adds value. While comprehensive, it is not overly verbose; a small improvement could be to condense the quality section slightly, but overall it is efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (7 parameters, multiple modes, model routing, external resource) and the presence of an output schema, the description is complete. It covers first-time reading, mode selection, parameter semantics, output handling, and edge cases (file existence, directory creation). No gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% (each parameter has a schema description), baseline is 3. However, the tool description adds substantial value beyond the schema: it provides structured prompt guidance, detailed size use cases, quality/fidelity defaults and recommendations, background handling rules, and file path requirements. This greatly aids correct parameter usage.

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?

The description clearly states the tool's purpose: 'Generate, edit, or compose images via OpenAI's gpt-image family.' It further distinguishes between generation, editing, and composition modes based on the references_paths parameter. No sibling tools exist, so differentiation is not needed.

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

Usage Guidelines5/5

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

The description explicitly advises reading a resource guide before the first call. It provides clear context for mode selection based on references_paths, model routing based on background, and when to set quality/fidelity. It covers prerequisites and usage scenarios thoroughly.

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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