imaginate-mcp

imaginate-mcp

Generates and edits images using OpenAI GPT Image or Google Gemini models, saving every result to disk and returning local file paths so AI assistants can continue working with the images. It enables prompt-based image creation, editing, inpainting, multi-image composition, and model listing through MCP tools.

Category
访问服务器

README

<p align="center"> <img src="logo.png" alt="Imaginate MCP logo" width="300" height="300"> </p>

imaginate-mcp

An MCP server that generates and edits images with OpenAI GPT Image and Google Gemini (Nano Banana). It runs over stdio, saves every image to disk, and hands back the file path so your assistant can keep working with the result.

What you get

Six tools, split by provider:

Tool What it does
openai_generate_image Text to image with GPT Image models
openai_edit_image Edit one image, inpaint with a mask, or compose several references
openai_list_image_models Model IDs, strengths, and limits
gemini_generate_image Text to image with Nano Banana models, with optional Google Search grounding
gemini_edit_image Edit, style transfer, semantic inpainting, or multi-image composition
gemini_list_image_models Model IDs, reference image limits, and resolution tiers

Only the tools for the keys you configure get registered. If you set OPENAI_API_KEY and nothing else, your assistant sees three tools and none of them can fail on a missing Google key. That was the main reason for splitting the tools by provider instead of using one tool with a provider argument.

Requirements

  • Node.js 20 or newer
  • An OpenAI API key, a Gemini API key, or both

GPT Image models need OpenAI API organization verification. If you have not done that, OpenAI rejects the request and the server tells you so.

Connect

Run the published package with npx. You do not need to clone the repository or install the package globally.

npx -y @pinkpixel/imaginate-mcp

Add the server to your client's config. For Claude Desktop, edit claude_desktop_config.json. For Claude Code, use .mcp.json in your project or your user settings.

{
  "mcpServers": {
    "imaginate": {
      "command": "npx",
      "args": ["-y", "@pinkpixel/imaginate-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "GEMINI_API_KEY": "...",
        "IMAGINATE_OUTPUT_DIR": "~/Pictures/imaginate"
      }
    }
  }
}

Restart the client after you edit the config. If no image tools appear, call imaginate_setup_help. That tool only exists when no provider key was found, and it lists the variables you still need to set.

Run from source

Clone and build the repository if you want to work on the server locally:

git clone https://github.com/pinkpixel-dev/imaginate-mcp.git
cd imaginate-mcp
npm install
npm run build
node dist/index.js

To connect an MCP client to this build, use "command": "node" and set args to the absolute path of dist/index.js.

Configuration

Every variable is read once at startup, so restart the client after you change one.

Variable Required Default What it does
OPENAI_API_KEY One key required none Registers the openai_* tools
GEMINI_API_KEY One key required none Registers the gemini_* tools. GOOGLE_API_KEY also works
IMAGINATE_OUTPUT_DIR no ~/Pictures/imaginate Where images are saved. A leading ~ is expanded
IMAGINATE_OPENAI_MODEL no gpt-image-2 Model used when a call does not name one
IMAGINATE_GEMINI_MODEL no gemini-3.1-flash-image Model used when a call does not name one
OPENAI_BASE_URL no OpenAI's default Point at an OpenAI-compatible proxy

Any tool call can override the output directory with output_dir and the file name with filename.

How the files work

Images go to the output directory. The server never overwrites anything. A file named cat.png that already exists becomes cat-1.png, then cat-2.png.

Default names look like openai-a-red-fox-20260825-134512-071.png. That is the provider prefix, a slug of your prompt, and a timestamp. Pass filename if you want something specific.

Source images for edits must be local files. Pass absolute paths. The tools do not download remote URLs, so fetch the file first if it lives on the web. Source files are read only and never modified.

Using it

Once the server is connected you mostly talk to your assistant normally. A few things worth knowing.

Picking a provider

Both providers are good, at different things.

Gemini is stronger on text inside images, world knowledge, and infographic work, and it can ground on live Google Search results before it draws. It also returns an interaction ID, so you can keep refining an image without uploading it again.

GPT Image follows detailed layout instructions well and gives you fine control over size, quality, and background. It is the one to use when you need a transparent background, though for that you need gpt-image-1.5 or older because gpt-image-2 dropped it.

Iterating on a Gemini image

Every Gemini result includes an interaction ID. Pass it back as previous_interaction_id on the next gemini_edit_image call and skip re-sending the image:

  1. gemini_generate_image with your prompt. The result includes an interaction ID.
  2. gemini_edit_image with previous_interaction_id and a prompt like "make it landscape."

This is cheaper than re-uploading and keeps the image more consistent between rounds.

Editing and composing

Both *_edit_image tools handle several jobs through the same interface. Pass one image path to edit that image. Pass several to combine them into a new scene.

For masked inpainting the two providers differ. OpenAI wants a real mask PNG with an alpha channel, passed as mask. Gemini does it semantically, so you just say "change only the sky and keep everything else exactly the same" and skip the mask file.

Reference image limits depend on the Gemini model: 14 on Lite, 10 on Nano Banana 2, 6 on Pro. Call gemini_list_image_models if you are not sure.

Development

npm run build      # compile to dist/
npm run watch      # compile on change
npm run typecheck  # types only, no output
npm test           # compile tests and run them

Tests use the built-in Node test runner. They cover the file naming and saving logic, the Gemini response parsing, and the error message mapping. They do not call either API, so you can run them without keys.

The layout:

src/
  index.ts               entry point, conditional tool registration
  config.ts              environment parsing
  lib/                   file handling, errors, result formatting, model catalog
  providers/openai/      OpenAI client wrapper and tool definitions
  providers/google/      Gemini client wrapper and tool definitions
tests/

Limitations

  • Source images must be local files. No remote URLs.
  • Streaming and partial images are not wired up. A call returns when the image is done.
  • Gemini does not reliably honor a requested image count, so ask for one image per call. The OpenAI tools take n and that works normally.
  • OpenAI can take up to two minutes on a complex prompt. That is the API, not the server.
  • Every Gemini image carries an invisible SynthID watermark.
  • Model IDs and pricing move fast on both providers. The list tools describe what this version knows about, which may drift from what your account can actually reach.

License

Apache 2.0. See LICENSE.

Made with 💖 by Pink Pixel

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选