Which integrations are available for this server?

Integrates with OpenAI's GPT-Image API to generate images from text prompts or transform existing images using text descriptions, supporting configurable size and quality.

How do I use GPT-Image MCP Server?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@GPT-Image MCP Server generate a photo of a sunset over the ocean and save to ~/Pictures/sunset.png" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

GPT-Image MCP Server

by IronManCantFix

Overview Schema Source Related Servers Discussions Admin

Python

Local

GPT-Image MCP Server

一个用于调用 GPT-Image 兼容 API 生成图片的 MCP 服务器，支持文生图和图生图功能。

兼容 Agnes AI 和 OpenAI GPT-Image 等标准接口。

✨ 功能特性

🎨 文生图：根据文本描述生成图片
🖼️ 图生图：基于参考图片和描述生成新图片
⚙️ 灵活配置：支持环境变量和配置文件，环境变量优先
🔌 标准协议：基于 MCP 协议，可集成到各种 AI 工具
📁 自动创建目录：保存图片时自动创建不存在的目录
🌐 多后端兼容：支持 Agnes AI、OpenAI 及其他兼容接口

Related MCP server: openai-gpt-image-1-mcp

📦 安装

方式一：使用 uvx 直接运行（推荐）

无需安装，直接运行：

uvx --from git+https://github.com/IronManCantFix/image-mcp.git image-mcp

方式二：使用 pip 安装

pip install git+https://github.com/IronManCantFix/image-mcp.git

安装后可直接使用 image-mcp 命令。

方式三：从源码安装

git clone https://github.com/IronManCantFix/image-mcp.git
cd image-mcp
pip install .

⚙️ 配置

Agnes AI（推荐）

export IMAGE_API_URL="https://apihub.agnes-ai.com/v1/images/generations"
export IMAGE_API_KEY="your-agnes-api-key"
export IMAGE_MODEL="agnes-image-2.0-flash"

OpenAI

export IMAGE_API_URL="https://api.openai.com/v1/images/generations"
export IMAGE_API_KEY="sk-your-openai-api-key"
export IMAGE_MODEL="gpt-image-2"

配置文件

也可以使用配置文件（默认路径 ~/.config/image-mcp/config.json）：

{
  "api_url": "https://api.openai.com/v1/images/generations",
  "api_key": "sk-your-api-key",
  "model": "gpt-image-2"
}

💡 提示：配置文件路径可通过 IMAGE_CONFIG_PATH 环境变量覆盖。

代理配置

如需通过 HTTP 代理访问 API，可设置 IMAGE_PROXY 环境变量或在配置文件中添加 proxy 字段：

export IMAGE_PROXY="http://127.0.0.1:7890"

配置文件方式：

{
  "api_url": "https://api.openai.com/v1/images/generations",
  "api_key": "sk-your-api-key",
  "model": "gpt-image-2",
  "proxy": "http://127.0.0.1:7890"
}

未配置时默认不使用代理，同时也会尊重系统环境变量 HTTP_PROXY / HTTPS_PROXY。

配置优先级

环境变量 > 配置文件 > 默认值

🚀 使用方法

启动 MCP 服务器

# 使用 uvx
uvx --from git+https://github.com/IronManCantFix/image-mcp.git image-mcp

# 使用 pip 安装后
image-mcp

# 从源码运行
python server.py

集成到 Cursor

在项目根目录创建 .cursor/mcp.json 文件：

{
  "mcpServers": {
    "image-mcp": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/IronManCantFix/image-mcp.git", "image-mcp"],
      "env": {
        "IMAGE_API_URL": "https://apihub.agnes-ai.com/v1/images/generations",
        "IMAGE_API_KEY": "your-agnes-api-key",
        "IMAGE_MODEL": "agnes-image-2.0-flash"
      }
    }
  }
}

或者使用 pip 安装后：

{
  "mcpServers": {
    "image-mcp": {
      "command": "image-mcp",
      "env": {
        "IMAGE_API_URL": "https://apihub.agnes-ai.com/v1/images/generations",
        "IMAGE_API_KEY": "your-agnes-api-key",
        "IMAGE_MODEL": "agnes-image-2.0-flash"
      }
    }
  }
}

集成到 Claude Desktop

在 Claude Desktop 配置文件中添加：

{
  "mcpServers": {
    "image-mcp": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/IronManCantFix/image-mcp.git", "image-mcp"],
      "env": {
        "IMAGE_API_URL": "https://apihub.agnes-ai.com/v1/images/generations",
        "IMAGE_API_KEY": "your-agnes-api-key",
        "IMAGE_MODEL": "agnes-image-2.0-flash"
      }
    }
  }
}

🛠️ 工具说明

text_to_image

根据文本描述生成图片。

参数	类型	必填	说明
`prompt`	string	✅	图片描述文本
`save_path`	string	✅	保存的本地文件路径
`size`	string	❌	图片尺寸，如 `1024x1024`，默认 `1024x1024`
`quality`	string	❌	图片质量：`low` / `medium` / `high`，默认 `medium`

示例：

{
  "prompt": "a cute cat sitting on a windowsill",
  "save_path": "/Users/me/images/cat.png",
  "size": "1024x1024",
  "quality": "high"
}

image_to_image

基于参考图片和文本描述生成新图片。

参数	类型	必填	说明
`prompt`	string	✅	变换描述文本
`input_image`	string	✅	输入图片的本地文件路径
`save_path`	string	✅	保存的本地文件路径
`size`	string	❌	输出图片尺寸
`quality`	string	❌	图片质量

示例：

{
  "prompt": "make it in anime style",
  "input_image": "/Users/me/images/original.png",
  "save_path": "/Users/me/images/anime_version.png",
  "size": "1024x1024",
  "quality": "high"
}

📤 返回格式

所有工具返回统一的 JSON 格式：

成功

{
  "success": true,
  "file_path": "/absolute/path/to/image.png",
  "message": "图片已保存"
}

失败

{
  "success": false,
  "error": "错误描述"
}

❓ 常见问题

Q: 如何获取 Agnes AI API Key？

A: 前往 Agnes AI API Hub 注册并获取 API Key。

Q: 如何获取 OpenAI API Key？

A: 前往 OpenAI Platform 创建 API Key。

Q: 支持哪些图片格式？

A: 输出格式为 PNG。图生图输入支持 PNG、JPG、JPEG、GIF、WebP 格式。

Q: 可以使用其他兼容的 API 吗？

A: 可以，只需将 IMAGE_API_URL 设置为对应的 API 端点地址，IMAGE_MODEL 设置为对应的模型名称即可。接口需兼容 OpenAI GPT-Image 的请求/响应格式。

Q: 图片保存在哪里？

A: 由调用时的 save_path 参数决定，可以是任意路径。如果目录不存在会自动创建。

📄 许可证

MIT License - 详见 LICENSE 文件

🔗 相关链接

Available Tools

2 tools

image_to_imageB

基于参考图片和文本描述生成新图片

Inputs: prompt (required), save_path (required), input_image (required), size, +1 more.

text_to_imageC

根据文本描述生成图片

Inputs: prompt (required), save_path (required), size, quality.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.4/5.0

Scored 2026-07-28 09:35 across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: one generates images purely from text, the other uses a reference image as input. There is no overlap.

Naming Consistency5/5

Both tools follow a consistent verb_noun snake_case pattern (text_to_image, image_to_image), making them predictable.

Tool Count4/5

With only 2 tools, the server is minimal but focused. The count is reasonable for a narrow image generation domain, though slightly thin.

Completeness3/5

The server covers basic text-to-image and image-to-image generation, but lacks common features like image editing (inpainting), variations, or parameter controls, leaving notable gaps.

Resources

Related MCP Connectors

NanoPic
net.nanopic
Generate and edit images (PNG, transparent, GIF, SVG) with GPT Image and Nano Banana models.
AI & Machine Learning Image & Video Processing
1
https://nanopic.net/api/mcp
Codex ImagemA
io.github.mcp-dir
Generate images with your own ChatGPT subscription (Plus, Pro or Team), without spending API credits
AI & Machine Learning Image & Video Processing
81
https://api.mcp.ai/p_codex_image
Claude ImagineA
com.claudeimagine
Generate AI images and videos from any compatible MCP client.
AI & Machine Learning Image & Video Processing
112
https://claudeimagine.com/api/mcp
TastepixA
io.github.paablobello
Generate images from Claude, ChatGPT, Cursor and Codex with 12 top AI image models. Up to 4 images per call, SVG vectorization, background removal, upscaling and image-to-video. Remote MCP with OAuth, no API keys.
AI & Machine Learning Image & Video Processing Multimedia Processing
101
https://mcp.tastepix.com/mcp

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GPT-Image MCP Server

✨ 功能特性

📦 安装

方式一：使用 uvx 直接运行（推荐）

方式二：使用 pip 安装

方式三：从源码安装

⚙️ 配置

Agnes AI（推荐）

OpenAI

配置文件

代理配置

配置优先级

🚀 使用方法

启动 MCP 服务器

集成到 Cursor

集成到 Claude Desktop

🛠️ 工具说明

text_to_image

image_to_image

📤 返回格式

成功

失败

❓ 常见问题

Q: 如何获取 Agnes AI API Key？

Q: 如何获取 OpenAI API Key？

Q: 支持哪些图片格式？

Q: 可以使用其他兼容的 API 吗？

Q: 图片保存在哪里？

📄 许可证

🔗 相关链接

Available Tools

Tool Schema Changelog

TDQS

Resources

Related MCP Connectors

Related MCP Servers

OpenAI GPT-Image MCP Server

openai-gpt-image-1-mcp

Agnes MCP Server

ImageForge MCP