Which integrations are available for this server?

Integrates with Ollama to run local vision models like LLaVA, allowing offline, private image recognition with zero API costs.

How do I use image-mcp?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@image-mcp describe the image at /home/user/photo.jpg" 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.

image-mcp

by systemmin

Overview Schema Source Related Servers Discussions Admin

TypeScript

Local

image-mcp · 视觉识别 MCP 服务器

一个基于 Model Context Protocol 的图片识别服务器，统一封装三大视觉 API 后端，让 Claude Desktop / Cursor / 任何 MCP 客户端都能调用图片描述、问答与多图分析能力。

📦 npm 包名:@systemmin/image-mcp(bin 命令 image-mcp),可直接 npx @systemmin/image-mcp 运行,无需克隆仓库。

✨ 支持的视觉后端

Provider	服务	适用场景
`anthropic`	Claude (Anthropic) API	用户提到的 "code plan API"，效果最强
`zhipu`	智谱 API (GLM-4V 等)	国产视觉模型，国内访问稳定
`ollama`	Ollama (本地 llava / llama3.2-vision)	本地离线运行，零成本、隐私好

所有后端通过统一的 provider 参数在运行时动态切换，无需重启。

Related MCP server: llm-vision-mcp

🧰 暴露的 MCP Tools

Tool	参数	说明
`vision_describe`	`path`, `provider?`	生成单张图片的详细文字描述/识别结果
`vision_qa`	`path`, `question`, `provider?`	针对图片提问，模型按图片内容回答 (VQA)
`vision_analyze`	`paths[]`, `prompt`, `provider?`	对多张图片综合分析/对比

provider 可选；不传时按 DEFAULT_PROVIDER -> anthropic -> zhipu -> ollama 优先级自动选择第一个已配置的后端。
返回结果开头会标注实际使用的 provider，例如 [provider: zhipu]。

📦 安装

方式一:直接用 npx(推荐,无需克隆)

npx @systemmin/image-mcp

或全局安装:

npm install -g @systemmin/image-mcp
image-mcp

方式二:从源码构建

# 需要 Node.js >= 20
git clone https://github.com/systemmin/image-mcp.git
cd image-mcp
npm install
npm run build

⚙️ 配置

复制 .env.example 为 .env 并填入密钥：

cp .env.example .env

# Claude (Anthropic)
ANTHROPIC_API_KEY=sk-ant-xxx
ANTHROPIC_MODEL=claude-sonnet-4-5-20250929

# 智谱
ZHIPU_API_KEY=xxx.xxx
ZHIPU_MODEL=glm-4v

# Ollama (本地)
OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_MODEL=llava

# 默认后端
DEFAULT_PROVIDER=anthropic

只需配置你想用的后端的密钥即可，其余可留空。Ollama 无需密钥，但要先 ollama pull llava 拉取视觉模型并保持服务运行。

⏱️ 工具调用超时(重要)

MCP_TOOL_TIMEOUT 是 MCP 客户端(Claude Code / Claude Desktop)侧的环境变量,不是本 server 的配置。视觉 API 处理大图较慢,若不调大超时,客户端往往在工具返回前就超时报错(实测大图极易触发)。

在启动客户端之前设置(单位:毫秒,下例为 5 分钟):

# Windows CMD
set MCP_TOOL_TIMEOUT=300000

# PowerShell
$env:MCP_TOOL_TIMEOUT="300000"

# macOS / Linux
export MCP_TOOL_TIMEOUT=300000

设好后再启动 claude 或 Claude Desktop;也可加入系统环境变量永久生效。

🔌 接入 MCP 客户端

Claude Code(命令行,推荐)

用官方命令一键添加,无需手改 JSON:

# 仅当前项目可用(local scope,默认)
claude mcp add image-mcp -- npx @systemmin/image-mcp

# 或全局所有项目可用(user scope)
claude mcp add image-mcp --scope user -- npx @systemmin/image-mcp

需要传入 API 密钥时用 -e(可多次):

claude mcp add image-mcp --scope user -e ANTHROPIC_API_KEY=sk-ant-xxx -e DEFAULT_PROVIDER=anthropic -- npx @systemmin/image-mcp

也可不传 -e,改为在你运行 claude 的目录放一个 .env 文件(dotenv 会自动加载)。验证是否注册成功:claude mcp list。

或手动编辑配置文件 ~/.claude.json(Windows: C:\Users\<用户名>\.claude.json,注意文件名带前导点),在顶层 mcpServers 中加入:

{
  "mcpServers": {
    "image-mcp": {
	  "type": "stdio",
      "command": "npx",
      "args": ["@systemmin/image-mcp"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-xxx",
        "DEFAULT_PROVIDER": "anthropic"
      }
    }
  }
}

Claude Desktop

编辑配置文件（macOS: ~/Library/Application Support/Claude/claude_desktop_config.json，Windows: %APPDATA%\Claude\claude_desktop_config.json）：

{
  "mcpServers": {
    "image-mcp": {
	  "type": "stdio",
      "command": "npx",
      "args": ["@systemmin/image-mcp"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-xxx",
        "ZHIPU_API_KEY": "xxx.xxx",
        "DEFAULT_PROVIDER": "anthropic"
      }
    }
  }
}

也可不写 env，改为在项目目录放 .env 文件（dotenv 会自动加载）。若从源码运行，把 command/args 换成 "command": "node", "args": ["/path/to/image-mcp/dist/index.js"]。

Cursor / 其他 MCP 客户端

按对应客户端文档添加一个 stdio 类型的 MCP server，命令为 npx @systemmin/image-mcp（全局安装后也可直接用 image-mcp）。

🧪 调试

用官方 Inspector 交互测试：

npm run inspector

会打开网页界面，可手动调用三个工具、查看请求/响应。

🏗️ 项目结构

src/
├── index.ts              # MCP 服务器入口，注册 3 个工具
├── providers/
│   ├── index.ts          # VisionProvider 接口 + getProvider 工厂
│   ├── anthropic.ts      # Claude API 适配器
│   ├── zhipu.ts          # 智谱 API 适配器
│   └── ollama.ts         # Ollama API 适配器
└── utils/
    ├── image.ts          # 图片读取 + base64 编码 + MIME 推断
    └── config.ts         # 环境变量加载 + provider 解析

架构说明

三个工具本质都调用 VisionProvider.analyze(images, prompt)，区别只在 prompt：

vision_describe -> 固定描述 prompt
vision_qa -> 用户问题作为 prompt
vision_analyze -> 多图 + 自定义 prompt

新增一个视觉后端只需实现 VisionProvider 接口并在 getProvider 注册即可。

📜 脚本

命令	作用
`npm run build`	TypeScript 编译到 `dist/`
`npm start`	运行编译后的服务器
`npm run inspector`	启动 MCP Inspector 调试

Available Tools

3 tools

vision_analyzeB

对多张本地图片进行综合分析/对比，根据自定义 prompt 给出结论

Inputs: paths (required), prompt (required), provider.

vision_describeB

对一张本地图片生成详细的文字描述/识别结果

Inputs: path (required), provider.

vision_qaA

针对一张本地图片提问，模型根据图片内容回答

Inputs: path (required), question (required), provider.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.8/5.0

Scored 2026-07-13 22:00 across 3 tools

Disambiguation5/5

Each tool targets a distinct task: multi-image analysis with custom prompt, single-image description, and single-image Q&A. No overlap in functionality.

Naming Consistency5/5

All tools follow consistent 'vision_<verb>' pattern with clear, descriptive verbs (analyze, describe, qa).

Tool Count5/5

Three tools is well-scoped for an image analysis server, covering the essential operations without bloat or insufficiency.

Completeness4/5

The set covers core image analysis tasks (description, QA, multi-image comparison). Missing potential features like image search or generation, but these are beyond the stated domain.

Maintenance

ActivityInactive

ResponsivenessNo recent community issues

Resources

Related MCP Connectors

mcp-qwen-image
io.github.AceDataCloud
MCP server for Qwen Image 3 AI image generation
AI & Machine Learning Image & Video Processing
1
https://qwen-image.mcp.acedata.cloud/mcp
OneOverB
com.oneover
Chat, image, video, and audio models from many vendors behind one remote MCP server and one key.
AI & Machine Learning Image & Video Processing Audio Processing
181
https://api.oneover.com/functions/v1/mcp
mcp-openai
io.github.AceDataCloud
MCP server for OpenAI API (chat completions, image generation, embeddings) via AceDataCloud
AI & Machine Learning Image & Video Processing
11
https://openai.mcp.acedata.cloud/mcp
mcp-wan
io.github.AceDataCloud
MCP server for Wan AI video generation
AI & Machine Learning Image & Video Processing
21
https://wan.mcp.acedata.cloud/mcp

Related MCP Servers

Luma MCP
AI & Machine Learning Image & Video Processing
JochenYang
A
license
A
quality
C
maintenance
Multi-model vision understanding MCP server that provides unified image analysis for AI assistants without native vision, supporting models like GLM-4.6V, DeepSeek-OCR, Qwen3-VL-Flash, and more.
Updated 3 months ago (2026-08-09 06:39 UTC)
1
664 npm
115
MIT
llm-vision-mcp
AI & Machine Learning Image & Video Processing
ghbalf
A
license
Not graded
quality
D
maintenance
An MCP server that enables any LLM to describe images from file paths, URLs, or base64 data by forwarding them to a supported vision provider such as OpenAI, Anthropic, or local Ollama models.
Updated 7 months ago (2026-04-21 22:53 UTC)
396 npm
10
MIT
vision-mcp
Image & Video Processing AI & Machine Learning
menoxz
A
license
Not graded
quality
D
maintenance
MCP server for analyzing images using multiple vision LLM providers (OpenCode, OpenAI, Anthropic, Google, and custom OpenAI-compatible endpoints). Provides tools to analyze single or multiple images, list providers, and test vision capabilities.
Updated 3 months ago (2026-08-01 07:12 UTC)
MIT
llm-vision
Image & Video Processing AI & Machine Learning
maple-red
A
license
Not graded
quality
D
maintenance
MCP server that provides a 'borrowed eye' for text-only LLMs, enabling them to identify and describe local images via the Qwen VL vision model, including face recognition, scene description, OCR, and targeted visual questioning.
Updated 3 months ago (2026-08-05 13:12 UTC)
7 npm
Apache 2.0