What can you do with this server?

This server connects AI tools (like Claude Code) to a text-to-image API, enabling AI-driven image generation directly from prompts. Generate images from text prompts (generate_image): * Preset resolutions: 1024x1024, 1024x1536, 1536x1024, 2048x1152, 2048x2048, 2160x3840, 3840x2160, or auto * Custom resolutions: Any WxH format (max 3840px per side, multiples of 16, aspect ratio ≤ 3:1) * Quality control: low, medium, high, or auto * Custom filenames: Specify a filename (without .png) or let it auto-generate * Async mode (default): Returns immediately while the image saves in the background * Synchronous mode: Waits for the image and returns it directly in the response * Custom output directory: Override where the image is saved List recently generated images (list_images): Browse images saved to the output directory, useful after async generation to check results. Supports a limit parameter and custom output directory. Images are saved as PNG files. Requires a MAGENE_API_KEY for authentication.

How do I use image2-mcp?

1. Click on "Install 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., "@image2-mcp Generate an image of a knight riding a dragon" 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.

image2-mcp

by Paipai112

Overview Schema Related Servers Score Discussions

Python

Remote

image2-mcp

MCP Server for the company image2 text-to-image API. Allows Claude Code, Codex, and other MCP-compatible AI tools to generate images directly.

🚀 Quick Start

自动安装（推荐）

git clone <repo-url>
cd image2-mcp
bash scripts/setup.sh

脚本会自动完成：

检查/安装 uv
安装 Python 依赖（uv sync）
引导你输入 API Key（或从 ~/.claude/.env 复用已有 key）
写入 ~/.claude/.env 环境配置
将 image2 注册到 ~/.claude.json（Claude Code 的 MCP 配置）
运行健康检查验证一切正常

完成后重启 Claude Code，说「帮我生成一张图片」即可。

手动安装

# 1. 安装依赖
uv sync

# 2. 配置环境变量
cp .env.example ~/.claude/.env
# 编辑 ~/.claude/.env，将 MAGENE_API_KEY 改为你的真实 key：
#   MAGENE_API_KEY=user_xxxxxxxx

# 3. 健康检查
uv run python -m image2_mcp --health-check

MCP 配置需要手动添加到 ~/.claude.json 的顶层 mcpServers 字段：

{
  "mcpServers": {
    "image2": {
      "command": "bash",
      "args": [
        "-c",
        "set -a; [ -f ~/.claude/.env ] && . ~/.claude/.env; [ -f .env ] && . ./.env; set +a; exec /Users/你的用户名/.local/bin/uv run --directory /path/to/image2-mcp python -m image2_mcp"
      ],
      "env": {
        "PATH": "/Users/你的用户名/.local/bin:/usr/local/bin:/usr/bin:/bin",
        "HOME": "/Users/你的用户名"
      },
      "description": "文生图 — 调用公司统一 API 平台 image2 模型生成图片",
      "type": "stdio"
    }
  }
}

⚠️ 注意将 /Users/你的用户名/ 替换为实际路径。uv 的路径可通过 which uv 获取。

Related MCP server: Nano-Banana MCP Server

⚙️ 环境变量

变量	必填	默认值	说明
`MAGENE_API_KEY`	是	—	公司 API 平台的 key
`MAGENE_API_BASE_URL`	否	`http://tops.magene.cn:11636/api/v1/images/generations`	API 地址
`IMAGE2_OUTPUT_DIR`	否	自动检测（项目 `/output`）	图片保存路径
`IMAGE2_MODEL`	否	`openai/gpt-image-2`	模型名称

图片输出目录的优先级：

显式参数
IMAGE2_OUTPUT_DIR 环境变量
CLAUDE_PROJECT_DIR → <项目>/output/（Claude Code 自动设）
IMAGE2_PROJECT_DIR → <项目>/output/（legacy）
当前工作目录 /output
系统临时目录 /image2-output

💡 通常无需手动设置任何目录变量——如果你在某个项目中用 Claude Code 打开，图片会自动存到那个项目的 output/ 文件夹。

📐 工具参数

参数	类型	必填	默认值	说明
`prompt`	string	是	—	图片描述（最长 32000 字符）
`size`	string	是	—	尺寸，见下方
`quality`	string	否	`auto`	`low` / `medium` / `high` / `auto`
`filename`	string	否	自动生成	自定义文件名（不含 `.png`）

可用尺寸：

值	分辨率
`1024x1024`	1K 方形
`1536x1024`	1.5K 横版
`1024x1536`	1.5K 竖版
`2048x2048`	2K 方形
`2048x1152`	2K 横版
`3840x2160`	4K 横版
`2160x3840`	4K 竖版
`auto`	自动

自定义尺寸：WxH 格式，每边 ≤ 3840px，16 的倍数，宽高比 ≤ 3:1，总像素 655,360–8,294,400。

🧪 开发

uv sync --group dev
uv run pytest --cov=src --cov-report=term-missing

🔧 工作原理

AI: generate_image(prompt="a cat in a garden", size="1024x1024")
    → 服务端校验参数
    → POST 到公司 API
    → 解码 Base64 响应
    → 写入 PNG 到磁盘
    → 返回图片 + 路径 + 用量统计

生成是异步的——AI 调用后立即返回，图片就绪后会通过日志通知。多张图片可并行生成。

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