vision-mcp
Allows the MCP server to use OpenAI's vision models for image understanding, OCR, and image processing through the OpenAI API.
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., "@vision-mcpWhat's in this image? /Users/me/Downloads/pic.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.
Vision MCP
让原本不具备视觉能力的纯文本模型,通过 MCP(Model Context Protocol)调用云端多模态模型,获得图像理解、图像问答、OCR、图像处理等视觉能力。
纯文本 LLM 无法直接"看"图,但可以通过 MCP 把这个 server 暴露成工具:
文本模型 ──MCP调用──▶ vision-mcp ──▶ OpenAI / Gemini / Claude(多模态 API)
│
├──▶ 图像理解(描述、问答、OCR)
└──▶ 图像处理(缩放、裁剪、旋转、转换、压缩、滤镜)功能
工具 | 说明 |
| 服务器状态、当前 provider / 模型、配置告警 |
| 详细描述图像内容 |
| 针对图像的任意自然语言问答 |
| 提取图中文字(OCR) |
| 本地处理:resize / crop / rotate / flip / convert / compress + 滤镜与亮度增强 |
Related MCP server: depu-img-mcp
快速开始
1. 克隆并安装
git clone <your-repo-url> vision-mcp
cd vision-mcp
python -m venv .venv
.venv\Scripts\pip install -r requirements.txt # Windows
# source .venv/bin/pip install -r requirements.txt # macOS / Linux2. 配置 API Key
复制 .env.example 为 .env,填入后端 API key:
VISION_PROVIDER=openai # openai | gemini | anthropic
OPENAI_API_KEY=sk-...
# 若使用 OpenAI 兼容端点(智谱 / DeepSeek 等):
# OPENAI_BASE_URL=https://open.bigmodel.cn/api/paas/v4/
# OPENAI_VISION_MODEL=glm-4.6v-flash只用你选择的 provider 对应的 key 即可。
3. 注册到 Claude Code
在 ~/.claude.json 或项目 .mcp.json 中添加:
{
"mcpServers": {
"vision-mcp": {
"command": "path/to/vision-mcp/.venv/Scripts/python.exe",
"args": ["-m", "vision_mcp.server"],
"cwd": "path/to/vision-mcp"
}
}
}4. (可选)上传图片自动分析
将 image_attach_hook.py 作为 UserPromptSubmit hook 注册到 ~/.claude/settings.json:
{
"hooks": {
"UserPromptSubmit": [
{
"hooks": [
{
"type": "command",
"command": "path/to/vision-mcp/.venv/Scripts/python.exe path/to/vision-mcp/image_attach_hook.py",
"timeout": 90
}
]
}
]
}
}上传图片时,hook 自动调用 describe_image + ocr_image,将视觉分析结果注入上下文——文本模型无需手动调用即可获得图片内容。
5. 手动测试
.venv\Scripts\python -m vision_mcp.server --help
# 或直接起 stdio 服务:
.venv\Scripts\python -m vision_mcp.server图像路径约定
传绝对路径;或
传相对路径,并设置
VISION_IMAGE_BASE_DIR指向图片目录;也可以直接传
data:URI(例如处理工具产出的结果)。
技术架构
vision_mcp/
├── config.py # 多 provider 配置(OpenAI / Gemini / Anthropic)
├── vision.py # 视觉后端:图像理解、问答、OCR
├── image.py # 图片加载、预处理、格式转换
├── processor.py # 本地图像处理(resize/crop/rotate/滤镜等)
├── server.py # MCP 服务入口,注册 5 个工具
└── __init__.py
image_attach_hook.py # UserPromptSubmit hook:上传图片自动分析
run_vision_mcp.bat # Windows 启动脚本
e2e_test.py # 端到端测试脚本
requirements.txt # Python 依赖许可
MIT
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