agent-vision
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@agent-visionAnalyze this image and tell me what it shows."
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.
agent-vision
一个小而直接的视觉 MCP Server + Agent Skill:让纯文本 Agent 通过一个 OpenAI-compatible 视觉模型理解图片和视频。
纯 Node.js / TypeScript,
npx自动下载运行MCP 工具:
analyze_image、analyze_videoAgent Skills 标准目录:
skills/agent-vision/图片:本地路径、HTTP(S) URL、Data URI
视频:本地路径或 URL,自动下载 npm 内的 ffmpeg 并均匀抽帧
单一 provider,无免费模型列表、fallback 竞速、watchdog 或透明代理
为什么做这个项目
参考项目各有优点,但组合起来偏重:
luma-mcp:Node.js、npx 和图片预处理体验好,但主要面向单图。
deepseek-vision-mcp:MCP 执行层与 Skill 决策层分离得很清楚,但依赖 Python,且本地视频受 provider 限制。
vision-tool:支持视频抽帧,但包含大量 provider 探测、并行 fallback 和安装逻辑,并采用 GPL-3.0。
agent-vision-toolkit:Skill 和任务工作流很强,但核心是多组 Python/Shell CLI,不是 MCP 视频服务。
agent-vision 只保留一条链路:MCP/CLI → 图片或视频帧 → 你配置的视觉 API → 文本。
本仓库是独立实现,没有复制 vision-tool 的 GPL 源码。
Related MCP server: VisionPower
要求
Node.js 20+
一个支持
/chat/completions和image_url的 OpenAI-compatible 视觉模型
ffmpeg 由 @ffmpeg-installer/ffmpeg 按平台自动安装,无需另行安装。
配置
export AGENT_VISION_BASE_URL="https://your-endpoint.example/v1"
export AGENT_VISION_API_KEY="your-key"
export AGENT_VISION_MODEL="your-vision-model"本地无鉴权端点可以不设置 API key。变量优先级:
设置 | 兼容回退 |
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可选变量:
变量 | 默认值 | 说明 |
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| 最大输出 tokens |
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| 下载和 API 超时 |
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| 图片大小上限 |
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| 视频大小上限 |
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| 额外请求头 JSON |
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| 允许私网图片/视频来源 URL |
| 自动发现 | 指定 ffmpeg 可执行文件;默认先用 PATH,再用 npm 内置版本 |
MCP 安装
npm(发布后推荐)
{
"mcpServers": {
"agent-vision": {
"command": "npx",
"args": ["-y", "@yanickxia/agent-vision"],
"env": {
"AGENT_VISION_BASE_URL": "https://your-endpoint.example/v1",
"AGENT_VISION_API_KEY": "your-key",
"AGENT_VISION_MODEL": "your-vision-model"
}
}
}
}直接从 GitHub 运行
{
"mcpServers": {
"agent-vision": {
"command": "npx",
"args": ["-y", "github:yanickxia/agent-vision"],
"env": {
"AGENT_VISION_BASE_URL": "https://your-endpoint.example/v1",
"AGENT_VISION_API_KEY": "your-key",
"AGENT_VISION_MODEL": "your-vision-model"
}
}
}
}Claude Desktop、Claude Code、Cursor、Cline 等使用上面的标准
mcpServers 格式。
OpenCode
{
"mcp": {
"agent-vision": {
"type": "local",
"command": ["npx", "-y", "@yanickxia/agent-vision"],
"enabled": true,
"environment": {
"AGENT_VISION_BASE_URL": "https://your-endpoint.example/v1",
"AGENT_VISION_API_KEY": "your-key",
"AGENT_VISION_MODEL": "your-vision-model"
}
}
}
}Agent Skill
npx skills add yanickxia/agent-vision --skill agent-vision -g -y也可以复制 skills/agent-vision/ 到 Agent 的 skills 目录。Skill 负责告诉
Agent 何时调用图片/视频工具、如何传参和如何处理失败;MCP 负责实际读取文件、
抽帧和请求模型。
CLI
npx -y @yanickxia/agent-vision image ./screenshot.png \
--prompt "读取报错并给出可能原因"
npx -y @yanickxia/agent-vision video ./demo.mp4 \
--prompt "按时间顺序总结 UI 操作" --frames 8
npx -y @yanickxia/agent-vision doctorMCP 工具
analyze_image
{
"source": "/absolute/path/to/image.png",
"prompt": "这个页面有哪些可用性问题?"
}支持 JPEG、PNG、WebP、GIF、BMP。GIF 是否能体现动画取决于视觉模型;需要稳定的
时间线分析时请转为视频并使用 analyze_video。
analyze_video
{
"source": "/absolute/path/to/video.mp4",
"prompt": "概括操作流程和关键变化",
"max_frames": 8
}max_frames 范围 1–16,默认 8。实现会均匀抽取 JPEG 帧,并带时间点一起发给
模型。它不是逐帧转写,快速变化可能被漏掉。
隐私与安全
图片会发送到你配置的视觉 API。
视频不会整体发给模型;远程视频先下载到本地临时目录,然后只发送抽出的 JPEG 帧。
临时目录在成功或失败后都会删除。
用户提供的远程图片/视频 URL 默认拒绝 localhost、私网、链路本地和云元数据地址。
stdio 模式不向 stdout 写日志,避免破坏 MCP 协议。
开发
npm install
npm run typecheck
npm test
npm pack --dry-runLicense
MIT。第三方依赖保留各自许可证,见 NOTICE。
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