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flux_edit_image

Edit an existing image with a text prompt to change elements, apply artistic styles, or add/remove objects. Use Flux kontext models for context-aware edits and style transfer.

Instructions

Edit an existing image using Flux with a text prompt.

This allows you to modify an existing image based on a text description.
The kontext models (flux-kontext-pro, flux-kontext-max) are specifically
designed for high-quality image editing and style transfer.

Use this when:
- You want to modify or transform an existing image
- You want to change specific elements in an image
- You want to apply style changes or artistic effects
- You want to add, remove, or replace objects in an image

For generating new images from scratch, use flux_generate_image instead.

Returns:
    Task ID and edited image information including URLs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeYesRequired output image size. For kontext models: aspect ratios like '1:1', '16:9'. For other models: pixel dimensions like '1024x1024'.
modelNoFlux model to use for editing. Recommended models for editing: - flux-kontext-pro: Best for context-aware editing and style transfer (recommended) - flux-kontext-max: Maximum context for complex edits - flux-dev: Basic editing support Other models also support editing but kontext models give best results.flux-kontext-pro
promptYesDescription of how to edit the image. Be specific about what changes to make. Examples: 'Change the background to a sunset beach', 'Add sunglasses to the person', 'Make it look like a watercolor painting', 'Replace the car with a bicycle'
image_urlYesURL of the image to edit. Must be a direct image URL (JPEG, PNG, etc.), not a web page containing an image.
callback_urlNoWebhook callback URL for asynchronous notifications.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed5 schema fields changedv0.1.9
    • removedInput schema / properties / size / anyOf
      Removed value: -[
      -  {
      -    "type": "string"
      -  },
      -  {
      -    "type": "null"
      -  }
      -]
    • removedInput schema / properties / size / default
      Removed value: -null
    • changedInput schema / properties / size / description
      Previous value: -"Output image size. For kontext models: aspect ratios like '1:1', '16:9'. For other models: pixel dimensions like '1024x1024'."New value: +"Required output image size. For kontext models: aspect ratios like '1:1', '16:9'. For other models: pixel dimensions like '1024x1024'."
    • addedInput schema / properties / size / type
      Added value: +"string"
    • changedInput schema / required
      Previous value: -[
      -  "prompt",
      -  "image_url"
      -]New value: +[
      +  "prompt",
      +  "image_url",
      +  "size"
      +]
  2. Changed1 schema field changedv0.1.7
    • changedInput schema / properties / model / enum
      Previous value: -[
      -  "flux-dev",
      -  "flux-pro",
      -  "flux-kontext-pro",
      -  "flux-kontext-max",
      -  "flux-2-flex",
      -  "flux-2-pro",
      -  "flux-2-max"
      -]New value: +[
      +  "flux-dev",
      +  "flux-pro",
      +  "flux-kontext-pro",
      +  "flux-kontext-max",
      +  "flux-2-flex",
      +  "flux-2-pro",
      +  "flux-2-max",
      +  "flux-2-klein"
      +]
  3. Addedv0.1.3
  4. Removedv0.1.2
  5. First observedv0.1.0

TDQS

A4.3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It explains the edit operation and mentions kontext model specifics but doesn't disclose async behavior (callback_url suggests it), rate limits, or auth requirements. Adequate but not rich.

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?

Well-structured with clear sections, front-loaded purpose, and a concise returns line. Slightly long but every sentence adds value for usage guidance.

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?

Covers purpose, usage, alternatives, and parameter guidance. The output schema exists and the return statement is brief; however, missing behavioral details (async, callback semantics) and no explicit when-not-to-use beyond generation, but sufficient for a complex multi-model tool.

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

Parameters4/5

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

Schema coverage is 100% with descriptions for all parameters. The description adds value by elaborating on recommended models and giving prompt examples beyond the schema, though not deeply for other params.

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 states 'Edit an existing image using Flux with a text prompt' with specific verbs and resource. It clearly distinguishes from flux_generate_image by explicitly noting the sibling for generation.

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?

Provides explicit 'Use this when' list with four concrete scenarios and names the alternative tool (flux_generate_image) for when not to use it.

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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