mimo-vision-mcp
OfficialProvides image recognition capabilities using Xiaomi's MiMo v2.5 multimodal model, allowing text-based agents to describe images, perform multi-image analysis, and extract text from images.
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., "@mimo-vision-mcpdescribe this screenshot: C:\Users\me\Desktop\bug.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.
mimo-vision-mcp
MCP Server:用小米 MiMo v2.5 多模态模型,为纯文本主模型(如 deepseek-v4-flash)补齐图像识别能力。
主模型没有视觉能力时,通过本 Server 把截图、UI 图、报错图、设计稿、照片等转成文字描述,
主模型据此继续推理。二次开发自 Mriestac/mimo-image-recognition-mcp(选型记录见下文)。
功能
工具 | 说明 |
| 单图理解(默认给详细描述) |
| 多图联合分析(前后对比 / A/B 方案) |
| 纯 OCR,保留换行缩进 |
| 只做本地校验,不调 API(排查输入问题) |
| 查看脱敏配置 |
图片输入支持:本地路径、http(s):// URL、file://、data:image/...;base64,...;
格式仅限 jpg/jpeg/png/gif/webp/bmp,单张 ≤10MB(官方限制)。
Related MCP server: Mimo Vision MCP
架构
主模型(纯文本)─ 图片路径/URL → MCP 工具
→ server 读图转 base64 → POST https://api.xiaomimimo.com/v1/chat/completions (mimo-v2.5)
→ 纯文本描述 → 主模型继续推理安装
cd <仓库路径> # 例如 C:\path\to\mimo-vision-mcp
uv sync --dev # 创建 .venv 并安装依赖(mcp[cli]<2、httpx、python-dotenv)
Copy-Item .env.example .env
# 编辑 .env 填入 MIMO_API_KEY注册到 Claude Code(全局)
cd <仓库路径> # 例如 C:\path\to\mimo-vision-mcp
$key = ((Get-Content .env | Where-Object { $_ -match '^MIMO_API_KEY=' }) -split '=', 2)[1]
claude mcp add mimo-vision -s user `
-e "MIMO_API_KEY=$key" `
-e "MIMO_BASE_URL=https://api.xiaomimimo.com/v1" `
-e "MIMO_VISION_MODEL=mimo-v2.5" `
-- "$PWD\.venv\Scripts\python.exe" "$PWD\server.py"验证:
claude mcp list # 应列出 mimo-vision
claude mcp get mimo-vision
claude mcp inspect mimo-vision # 协议级连通性自检主会话中需 重启 Claude Code 或 /mcp 重连 后工具才出现。
两种 Key 的差异
Key 类型 | 前缀 |
|
普通按量付费 |
|
|
Token Plan |
|
|
mimo-v2.5-pro 是纯文本推理模型,视觉理解必须用 mimo-v2.5。
使用
主会话中对模型说,例如:
描述这张图:C:\path\to\your\image.png 提取这张报错截图里的文字:C:\path\to\error.png
模型会自动调用对应工具。
验证
uv run --env-file .env pytest tests/test_image_utils.py tests/smoke_test.py # 离线单测
uv run --env-file .env pytest tests/test_api.py # 真实 API 直连踩坑记录
MiMo API 硬性要求:content 数组必须同时包含
image_url与text对象, 角色必须user,否则返回400 Param Incorrect - text is not set(见api_client.build_vision_message)。mcp SDK 2.x 移除了
mcp.server.fastmcp:pyproject 锁定mcp[cli]>=1.2,<2.0。mcp 1.29 lifespan 是构造函数参数(非
@mcp.lifespan_context);工具/资源通过参数注解Context注入。resource 有函数参数会被注册为模板资源(
_templates)而非普通资源,list_resources看不到——配置资源改为无参数函数。认证头用
Authorization: Bearer(base 项目 Mriestac 原用api-key,已修正);单图大小上限按官方改 10MB;补充file://输入支持。
配置项(.env)
变量 | 默认 | 说明 |
| — | 必填 |
|
| Token Plan 需改 |
|
| 视觉模型名 |
|
| 最大输出 token |
|
| 请求超时(秒) |
|
| 是否输出思考过程(更慢) |
|
| 单图上限(10MB) |
选型记录
候选仓库(均已 clone 审阅后删除 _ref/):
选定 base:
Mriestac/mimo-image-recognition-mcp—— 天然用 OpenAIchat/completions格式 +api.xiaomimimo.com,依赖轻(httpx),async 实现,工具/资源写法为标准 FastMCP。备选:
kuohao233/mimo-vision-mcp—— 工具更全(describe/analyze/ocr)但走 Anthropic/v1/messages格式,重写请求层成本高;其工具设计(默认 prompt、OCR 提示词)已借鉴到本项目。
修复自 base 的 3 处问题:认证头、10MB 上限、file:// 支持,并新增 read_image_info 工具与 4 个独立工具拆分。
Maintenance
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