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image-mcp

by OoadaioO
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image-mcp

基于 DashScope 视觉模型的本地 MCP server，用于识别本地或远程图片，并默认以 XML 形式返回图片内容，帮助 AI 理解图片。

安装

npm install

Related MCP server: image-mcp

MCP 配置

模型配置通过 MCP client 的 env 注入到 server 进程，不需要也不应该作为 tool 参数传递。

{
  "mcpServers": {
    "image-mcp": {
      "command": "node",
      "args": ["/absolute/path/to/image-mcp/src/index.mjs"],
      "env": {
        "DASHSCOPE_API_KEY": "your-api-key",
        "VISION_MODEL": "qwen-vl-plus",
        "DASHSCOPE_BASE_URL": "https://dashscope.aliyuncs.com/compatible-mode/v1"
      }
    }
  }
}

DASHSCOPE_BASE_URL 可省略，默认使用 https://dashscope.aliyuncs.com/compatible-mode/v1。

Tool

recognize_image

  • image_path: 本地图片路径，和 image_url 二选一。

  • image_url: 远程图片 URL，和 image_path 二选一。

  • prompt: 可选识图提示词，默认值为 识别图像内容，使用xml形式输出，帮助ai理解图片内容。

  • max_tokens: 可选最大输出 token 数，默认 1024。

CLI

npm run vision -- ./dist/ScreenShot_2026-08-01_155219_072.png "识别图像内容，使用xml形式输出，帮助ai理解图片内容"
npm run vision -- --url https://example.com/image.png

本地 npm 使用

如果只是本机使用，不需要发布到 npm registry。推荐用 npm link 或 npm pack。

方式一：npm link

在项目目录执行：

npm install
npm link

然后 MCP 配置可以直接使用全局命令：

{
  "mcpServers": {
    "image-mcp": {
      "command": "image-mcp",
      "args": [],
      "env": {
        "DASHSCOPE_API_KEY": "your-api-key",
        "VISION_MODEL": "qwen-vl-plus",
        "DASHSCOPE_BASE_URL": "https://dashscope.aliyuncs.com/compatible-mode/v1"
      }
    }
  }
}

如果之后修改了源码，一般不需要重新 link；重启 MCP client 即可加载新代码。

取消本地链接：

npm unlink -g image-mcp

方式二：npm pack

如果想模拟正式 npm 包安装，但仍然只在本地使用：

npm install
npm pack
npm install -g ./image-mcp-1.0.0.tgz

安装后同样可以在 MCP 配置中使用：

{
  "mcpServers": {
    "image-mcp": {
      "command": "image-mcp",
      "args": [],
      "env": {
        "DASHSCOPE_API_KEY": "your-api-key",
        "VISION_MODEL": "qwen-vl-plus"
      }
    }
  }
}

升级本地包时重新执行：

npm pack
npm install -g ./image-mcp-1.0.0.tgz

检查命令

确认全局命令可用：

which image-mcp
image-mcp

image-mcp 是 MCP stdio server，直接运行后会等待 MCP client 输入；没有输出不代表失败。实际验证建议在 MCP client 中查看是否能列出 recognize_image 工具。

特殊说明

项目扩展自 : https://github.com/asuojun/claude-vision-skill/tree/master

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