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AI Image Editor MCP Server

by rocnubie
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AI Image Editor MCP Server

AI Image Editor Free Online | Image to Image AI Photo Editor

MCP Badge License: MIT Node Zero Config Read Only MCP

A Model Context Protocol server that exposes the canonical AI Image Editor knowledge surface — image generation workflows and styles, pricing, FAQ, official links — to MCP-compatible AI clients such as Claude Desktop, Cursor, Windsurf, and Continue. Read-only, no API keys, no quota, ~50 ms cold start.

Official website: https://ai-image-editor.online

🎨 About AI Image Editor

AI Image Editor (https://ai-image-editor.online) is a browser-based image creation and editing platform that lets users generate visuals from text prompts or transform existing photos using AI. The site supports both text-to-image and image-to-image workflows, making it possible to produce original artwork, edit photographs, or create product visuals without installing any software. It runs multiple underlying AI models — including Flux, Seedream, Qwen, and Nano Banana Pro — and offers style presets ranging from photorealism to anime, Ghibli, and cyberpunk. The platform positions itself as a free alternative to paid tools, with no subscription required and no hard cap on output resolution.

Related MCP server: Gemini Image MCP Server

Key Features

  • Text-to-image generation — describe a scene or subject in plain language and receive a high-resolution image in seconds, with control over style and aspect ratio.

  • Image-to-image editing — upload an existing photo as a reference and apply transformations such as style transfer, background replacement, or object modification.

  • Face swap — replace faces in portraits or group photos while preserving lighting and skin tone.

  • Background removal and replacement — isolate subjects from their backgrounds automatically, then place them on custom or AI-generated scenes.

  • Batch generation — produce multiple image variants from a single prompt in one request, useful for A/B testing or rapid iteration.

  • Image-to-video conversion — animate a still image into a short video clip, extending generated content into motion formats.

  • API access — developers can integrate image generation into their own applications through a documented API.

Use Cases

  • E-commerce product photography — generate clean, studio-quality product shots without a photo shoot, or swap backgrounds to match seasonal campaigns.

  • Social media content — create on-brand visuals for posts, thumbnails, or stories quickly, iterating across different styles until the result fits the brief.

  • Creative exploration — artists and designers can use the style presets and image-to-image mode to prototype visual directions before committing to a final production pipeline.

  • Portrait retouching and face swap — swap faces for composite images, update headshots, or create character variations for games and storytelling projects.

  • Application prototyping — developers can call the API to test AI image generation inside their own products before choosing a permanent provider.

Who Is It For

AI Image Editor is aimed at a broad range of users who need AI-generated visuals without the overhead of a paid subscription or a steep learning curve. E-commerce merchants who need product imagery on a tight budget, social media managers producing high volumes of content, and independent designers experimenting with generative aesthetics will find the free tier practical for day-to-day work. The API access also makes it relevant for developers building image-generation features into web apps or automation pipelines. The platform's multiple model options and style presets give technically inclined users enough control to tune outputs, while the straightforward prompt interface keeps it accessible to non-designers who simply want a usable image fast.

Tools

list_styles

Return the canonical list of image-generation styles or presets the site exposes. (AI Image Editor)

Input: no parameters. Returns: text/markdown.

get_pricing

Return the canonical pricing entry point for AI Image Editor.

Input: no parameters. Returns: text/markdown.

get_official_links

Return the canonical list of official links for AI Image Editor (website, support, docs when available).

Input: no parameters. Returns: text/markdown.

Resources

  • site://aiimageeditor/styles — Supported image-generation styles and presets.

  • site://aiimageeditor/pricing — Canonical pricing entry point.

  • site://aiimageeditor/faq — Short FAQ generated from public site metadata.

  • site://aiimageeditor/links — Canonical URLs to share with users.

Prompts

tell_me_about_aiimageeditor

Summarize what the site is, who it's for, and how it works. — AI Image Editor

try_image_style_aiimageeditor

Recommend a starting image-generation style for a stated goal. — AI Image Editor

Installation

Install via Smithery

npx -y @smithery/cli install aiimageeditor-mcp --client claude

(Replace claude with cursor, windsurf, or continue for those clients.)

Install from source

git clone https://github.com/rocnubie/aiimageeditor-mcp.git
cd aiimageeditor-mcp
pnpm install

Then add to your MCP client config (claude_desktop_config.json for Claude Desktop, mcp.json for Cursor / Windsurf / Continue):

{
  "mcpServers": {
    "aiimageeditor-mcp": {
      "command": "node",
      "args": [
        "/absolute/path/to/aiimageeditor-mcp/src/index.mjs"
      ]
    }
  }
}

Debug with MCP Inspector

npx @modelcontextprotocol/inspector node src/index.mjs

Official Links

Development

pnpm install
pnpm start                 # run the server over stdio

License

MIT

Available Tools

3 tools
get_pricingB

Return the canonical pricing entry point for AI Image Editor.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are present, and the description does not disclose behavioral traits such as side effects, permission requirements, or response characteristics. It only states the tool returns data, leaving agents uninformed about what happens during invocation.

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?

The description is a single sentence with no unnecessary words. It is front-loaded and efficient, earning its place without redundancy.

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

Completeness3/5

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

For a tool with no parameters and no output schema, the description is minimal. It explains the purpose but does not specify the structure or format of the return value, which would help agents interpret the result. Some additional context about what 'canonical pricing entry point' means would improve completeness.

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?

The input schema has zero parameters with 100% description coverage, so the description need not add parameter details. The baseline for no parameters is 4, and no additional information is required.

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 clearly states the tool returns the canonical pricing entry point for AI Image Editor, which differentiates it from siblings dealing with styles and official links. However, 'pricing entry point' is somewhat ambiguous; specifying whether it returns a URL, document, or other format would improve clarity.

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

Usage Guidelines2/5

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

No usage guidelines are provided. The description does not specify when to use this tool versus alternatives, nor does it mention prerequisites or context. The need for pricing information is implied but not explicitly guided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_stylesA

Return the canonical list of image-generation styles or presets the site exposes. (AI Image Editor)

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A3.8/5.0
Behavior2/5

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

No annotations present, so description carries full burden. It indicates a read operation ('Return') but omits details like authentication, caching, or ordering.

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?

Single efficient sentence plus parenthetical clarification; no redundant information.

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 parameterless list tool with no output schema, the description sufficiently explains what the tool returns; minor omission of auth requirements.

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?

With zero parameters and 100% schema coverage, description adds context by specifying 'canonical list' and 'image-generation styles or presets', enriching the empty 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?

Description clearly states the verb 'Return' and the resource 'canonical list of image-generation styles or presets', distinguishing it from siblings get_pricing and get_official_links.

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?

Implied usage for retrieving available styles; no explicit when-to-use or alternatives provided, though siblings are unrelated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 3 tool updatesv0.1.0
    • First observedget_official_links
    • First observedget_pricing
    • First observedlist_styles
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TDQS

A3.6/5.0

Scored across 3 tools

Disambiguation5/5

Each tool targets a distinct information category (styles, pricing, links) with no overlap, making them easily distinguishable.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (list_styles, get_pricing, get_official_links), using similar verbs and clear nouns.

Tool Count4/5

Three tools is a reasonable count for a simple informational server, though slightly low for a full-featured editor.

Completeness2/5

The server lacks core editing or generation tools expected from an 'AI Image Editor', leaving significant gaps in functionality.

Resources

Unclaimed servers have limited discoverability.

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