visual-understand-mcp
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., "@visual-understand-mcpWhat does this screenshot show?"
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.
视觉理解 MCP Server
为不支持视觉的编码模型提供图片理解能力。
原理
编码模型(如 GLM-5.1)遇到图片时,自动调用 MCP 工具 understand_image,由视觉模型完成图片识别,结果以文本回传给编码模型继续推理。无需切换模型,上下文不中断。
用户粘贴截图 → 编码模型调用 understand_image → 视觉模型识别 → 文字描述回传 → 继续编码Related MCP server: vision-mcp
安装方式一:通过 PyPI / uvx 使用(推荐)
MCP 配置示例:
{
"mcpServers": {
"visual-understand": {
"command": "uvx",
"args": ["visual-understand-mcp"],
"env": {
"VISION_API_BASE": "https://dashscope.aliyuncs.com/compatible-mode/v1",
"VISION_MODEL": "qwen-vl-max",
"VISION_API_KEY": "sk-xxx"
}
}
}
}安装方式二:本地源码运行
{
"mcpServers": {
"visual-understand": {
"command": "uv",
"args": [
"--directory", "/path/to/visual-understand-mcp",
"run", "visual-understand-mcp"
],
"env": {
"VISION_API_BASE": "https://dashscope.aliyuncs.com/compatible-mode/v1",
"VISION_MODEL": "qwen-vl-max",
"VISION_API_KEY": "sk-xxx"
}
}
}
}配置项
变量 | 说明 | 默认值 |
| 视觉模型 API 地址 | 无(必填) |
| 视觉模型名称 | 无(必填) |
| 视觉模型密钥 | 无(必填) |
| 输出随机性 |
|
| 最大输出长度 |
|
| 请求超时(秒) |
|
| 视觉模型系统提示词 | 见下方 |
前三个必填,其余可选。配置也可写入 ~/.visual-understand-mcp/config.json,env 优先级更高。
默认系统提示词:
你是一个图片分析助手,仅用于理解图片内容。请按以下结构分析图片,根据实际内容调整详细程度:1. 文字内容 — 逐字提取图中所有可见文字,保留原始格式和层级关系。2. 错误信息与代码 — 如果图中包含错误信息或代码片段,必须原样输出,不做任何改写或概括;如果没有则忽略此项。3. 视觉布局与元素 — 描述空间排列、尺寸、颜色及关键元素间的关系。UI 截图请识别组件类型及其状态。4. 数据与指标 — 图表、表格请提取数值、坐标轴、标签、趋势及异常数据点。5. 整体概述 — 概括图片的主题、场景和关键信息,提供完整的上下文理解。不适用的部分跳过。只描述图片中可见的内容,不做推理、猜测或延伸解读。保持精确客观,不推测不可见的内容。
建议写入 CLAUDE.md
进行图片识别任务时,使用 visual-understand MCP 的 understand_image 工具。本地调试
uv run mcp dev src/visual_understand_mcp/server.pyLicense
MIT
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Tools
Related MCP Servers
- Flicense-qualityCmaintenanceAnalyzes images using a vision AI model to provide detailed descriptions, enabling non-vision models to understand image content.Last updated
- AlicenseAqualityBmaintenanceGives text-only coding agents the ability to 'see' images, videos, and screenshots by routing them to a vision model and returning structured text.Last updated837MIT
- FlicenseAqualityBmaintenanceGives any MCP client (OpenCode, Claude Code, Claude Desktop, Cursor, etc.) the ability to process images by automatically converting them to text descriptions using a vision model, so that text-only LLMs can handle image-based queries.Last updated2
- AlicenseAqualityAmaintenanceEnables text-only coding agents to analyze local images using a dedicated vision provider, returning markdown and structured JSON evidence for screenshots, diagrams, UI mockups, and error captures.Last updated111705MIT
Related MCP Connectors
Browser-backed QA with evidence and fix-ready reports for coding agents.
OCR, transcription, file extraction, and image generation for AI agents via MCP.
Generate images, GIFs, and PDFs from HTML, URLs, or templates — from your AI agent.
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/bcdxc/visual-understand-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server