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openai_edit_image

Modify an existing image by providing a reference image URL and a text description of the desired changes. Uses AI to add, remove, or transform elements.

Instructions

Edit or modify existing images using OpenAI image models via AceDataCloud.

Applies AI-powered edits to existing images based on text descriptions.
Can modify, extend, or transform images while preserving desired elements.

Use this when:
- You want to modify an existing image
- You need to add, remove, or change elements in an image
- You want to apply a specific style or transformation to an image

Returns:
    JSON response containing the edited image URL(s) or base64 data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNoNumber of images to generate (1-10). Default is 1.
sizeNoOutput image dimensions as 'WIDTHxHEIGHT' or 'auto'. Default is '1024x1024'. gpt-image-2 accepts any custom dimensions (multiples of 16, longer side ≤ 3840, total pixels ≤ 8,294,400). Common presets — 1K: '1024x1024', '1536x1024', '1024x1536', '1792x1024', '1024x1792'; 2K (1.5× rate): '2048x2048', '2048x1536', '1536x2048', '2048x1152', '1152x2048'; 4K (1.5× rate): '2880x2880', '3264x2448', '2448x3264', '3840x2160', '2160x3840'. dall-e-2: '256x256', '512x512', '1024x1024'.1024x1024
imageYesReference image URL(s). Accepts a single URL string or an array of URLs for multi-image editing. The image(s) to use as the starting point for edits.
modelNoThe image model to use for editing. Options: 'gpt-image-1' (default), 'gpt-image-1.5', 'gpt-image-2', 'dall-e-3', 'nano-banana' variants.gpt-image-1
promptYesText description of the desired edit. Max 1000 characters for gpt-image models. Describe what you want to change or add to the image.
qualityNoOutput quality. Options: 'auto' (default), 'high', 'medium', 'low', 'standard'.auto
backgroundNoBackground handling. 'transparent' removes background, 'opaque' keeps it, 'auto' decides automatically.
callback_urlNoOptional webhook URL. When provided, returns task_id immediately and POSTs result to this URL when complete.
output_formatNoOutput file format. Options: 'png' (default), 'jpeg', 'webp'.png
input_fidelityNoHow closely to follow the reference image. 'high' preserves more detail, 'low' allows more creative freedom.
response_formatNoHow to return the image. 'url' (default) returns a URL, 'b64_json' returns base64-encoded image data.url
output_compressionNoCompression level (0-100%) for jpeg/webp output. Default is 100.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so description carries full burden. It mentions AI-powered edits and returns JSON with image URL/base64, but does not disclose important behaviors like model-specific limitations, potential failures, or the irreversible nature of edits. It adds contextual value but lacks comprehensive transparency.

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 front-loaded with purpose and use cases, with bullet points for clarity. It is relatively concise, though the mention of 'AceDataCloud' clutters slightly. The return format section is brief. Overall efficient for the complexity.

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?

Given the tool has 12 parameters and an output schema, the description covers use cases, return format, and high-level capabilities. It could be more complete by explaining model-specific behavior (e.g., size presets for different models) and multi-image editing support, but it is generally adequate.

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 baseline is 3. The description does not add extra parameter information beyond what the schema provides. It mentions broad capabilities but no parameter-specific details beyond the schema.

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 verb 'Edit or modify existing images' and the resource 'existing images using OpenAI image models via AceDataCloud'. It distinguishes itself from sibling tool openai_generate_image by specifying that it works on existing images.

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?

Explicitly lists when to use this tool with three bullet points: modify existing image, add/remove/change elements, apply style/transformation. This helps an AI agent select it over alternatives.

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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