A
licenseA
qualityD
maintenanceEnables image analysis and understanding using Vision Language Models through OpenAI-compatible APIs. Supports analyzing images from URLs or local files with custom prompts.
1
2
MIT
mcp-vision-server
Provides tools for image understanding, OCR, and image generation through OpenAI-compatible APIs.
Click on "Deploy 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., "@mcp-vision-server extract text from this screenshot"
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.
MCP-Vision
基于 goehou/Visual-Enhancement-mcp（MIT）魔改的 MCP 服务，接入 OpenAI 兼容 API，提供看图、识字、生图。
工具
工具 | 做什么 |
| 通用图片理解 |
| 逐字提取图片文字 |
| 文本生成图片（多张/尺寸/模型覆盖） |
图片传法三选一：imagePath（本地路径）/ imageUrl / imageBase64 + imageMediaType。
Related MCP server: AI Image MCP Server
配置
模型优先级：调用时传 model > 工具专用 > 全局默认。
环境变量 | 说明 | 默认值 |
| 视觉 API 根地址 | 必填 |
| 视觉 Key | 必填 |
| 默认视觉模型 | 必填 |
| analyze 专用模型 | 回退 |
| ocr 备用视觉平台地址 | 回退 |
| ocr 备用视觉 Key | 回退 |
| ocr 备用视觉模型 | 回退 |
| 生图 API 根地址 | 回退 |
| 生图 Key | 回退 |
| 生图模型 | 回退 |
| 视觉超时（毫秒） |
|
| 生图超时（毫秒） |
|
OpenCode 配置：
{
"mcp": {
"vision": {
"type": "local",
"command": ["mcp-vision-server"],
"environment": {
"VISION_API_BASE_URL": "...",
"VISION_API_KEY": "...",
"VISION_MODEL": "...",
"IMAGE_API_BASE_URL": "...",
"IMAGE_API_KEY": "...",
"IMAGE_MODEL": "..."
},
"enabled": true
}
}
}相比原项目
新增
image_generate生图工具（多张/尺寸/模型覆盖，URL 下载加固）新增
VISION_BACKUP_*备用视觉端点新增
VISION_ANALYZE_MODEL工具级模型覆盖加固：仅允许 http/https 下载、base64 大小上限、错误信息截断、多图全返回、空配置可启动
安装
# 方式一：本地克隆后安装
git clone https://github.com/xiaoshengyvlin/MCP-Vision.git
cd MCP-Vision
npm install -g .
# 方式二：tarball 安装
npm pack && npm install -g mcp-vision-server-0.1.4.tgzAI 安装（推荐）
Available Tools
3 toolsimage_generateGenerate imagesA
Generate one or more images from a text prompt through an OpenAI-compatible images/generations endpoint. Images are returned inline as base64 when available, otherwise as download URLs.
Inputs: prompt (required), n, size, model. Returns: text, count, model
vision_analyzeAnalyze an image with a vision modelA
Understand, explain, or describe an image using a vision model. Use for interpreting screenshots, UI, diagrams, charts, or error messages that need reasoning. Not for verbatim text extraction (use vision_ocr).
Inputs: prompt (required), model, detail, imageUrl, +4 more. Returns: text, model, mediaType, sourceLabel
vision_ocrExtract text from an imageA
Extract exact text from an image (OCR). Use when the user wants literal text copied verbatim from a screenshot, code image, terminal output, document, or receipt. Not for explaining or understanding images (use vision_analyze).
Inputs: model, detail, imageUrl, imagePath, +5 more. Returns: text, model, mediaType, sourceLabel
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
A4.4/5.0
Scored across 3 tools
Disambiguation5/5
Each tool has a clearly distinct purpose: vision_analyze for understanding/explaining images, vision_ocr for verbatim text extraction, and image_generate for creating images. The descriptions explicitly cross-reference each other to prevent confusion, leaving no ambiguity.
Naming Consistency4/5
The naming is mostly consistent with a domain prefix and action (vision_analyze, vision_ocr), but image_generate deviates by using 'image_' instead of 'vision_'. This is a minor inconsistency that does not hinder readability, but it is a noticeable break from the established pattern.
Tool Count5/5
Three tools is a well-scoped count for a vision server, covering analysis, OCR, and generation. Each tool addresses a distinct core capability, and there are no redundant or missing tools that would suggest over- or under-engineering.
Completeness5/5
For the stated domain of vision tasks, the tool surface is complete: understanding (analyze), text extraction (OCR), and creation (generate). There are no obvious missing operations that would force an agent into a dead end; the tools cover the primary workflows one would expect from a vision server.
Maintenance
ActivityInactive
ResponsivenessOutdated data
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