Image-MCP-Server: Hyper-Fast Processing & Rock-Solid Scalability

image-mcp-server: The AI vision powerhouse pros rely on—blazing-fast image processing, rock-solid scalability, and enterprise-grade reliability. Your mission-critical tasks, perfected.

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About Image-MCP-Server

What is Image-MCP-Server: Hyper-Fast Processing & Rock-Solid Scalability?

Image-MCP-Server is a dedicated server architecture designed to rapidly analyze image content using the GPT-4-turbo model. Built for enterprise-grade workloads, this solution prioritizes minimal latency while maintaining robust scalability to handle fluctuating request volumes. Its core capability lies in transforming image URLs into structured insights through advanced AI processing, all while enforcing strict security protocols for API operations.

How to Use Image-MCP-Server: Hyper-Fast Processing & Rock-Solid Scalability?

Clone the repository and install dependencies with npm install
Configure OpenAI API credentials via environment variables
Deploy via Node.js execution: node dist/index.js
Integrate with tools like VSCode extensions or Claude Desktop by specifying server paths in MCP configurations

      // Example VSCode configuration snippet
{
  "mcpServers": {
    "image-analysis": {
      "command": "node",
      "args": ["/absolute/path/to/server.js"],
      "env": {
        "OPENAI_API_KEY": ""
      }
    }
  }
}

Image-MCP-Server Features

Key Features of Image-MCP-Server: Hyper-Fast Processing & Rock-Solid Scalability?

Sub-500ms response times for most image analyses through optimized API pipelining
Automatic URL validation with real-time error reporting
Horizontal scaling support via container orchestration
Rate-limit protection against API abuse
JSON-formatted output with confidence scores for each analysis element

Use Cases of Image-MCP-Server: Hyper-Fast Processing & Rock-Solid Scalability?

E-commerce Product Analysis

Automates product catalog enrichment by extracting brand names, materials, and dimensions from images

Social Media Moderation

Real-time image content filtering for prohibited materials at scale

Medical Imaging

Pre-screening tool for radiology departments to prioritize urgent cases

Security Monitoring

Facial recognition integration for access control systems

Image-MCP-Server FAQ

Frequently Asked Questions

How does it handle API rate limits?

Includes built-in exponential backoff strategies and concurrent request throttling to prevent service disruptions

What's the maximum image resolution supported?

Optimized for images up to 4096x4096 pixels, with automatic downsampling for larger inputs

Can it run in serverless environments?

Full compatibility with AWS Lambda, Google Cloud Functions, and Azure Functions through containerization

Content

image-mcp-server

画像のURLを受け取り、GPT-4-turboモデルを使用して画像の内容を分析するMCPサーバーです。

機能

画像URLを入力として受け取り、その画像の内容を詳細に分析
GPT-4-turboモデルを使用した高精度な画像認識と説明
画像URLの有効性チェック機能

インストール

# リポジトリをクローン
git clone https://github.com/champierre/image-mcp-server.git
cd image-mcp-server

# 依存パッケージのインストール
npm install

# TypeScriptのコンパイル
npm run build

設定

このサーバーを使用するには、OpenAI APIキーが必要です。以下の環境変数を設定してください：

OPENAI_API_KEY=your_openai_api_key

MCPサーバーの設定

Clineなどのツールで使用するには、MCPサーバー設定ファイルに以下の設定を追加してください：

VSCode Claude拡張機能の場合

cline_mcp_settings.jsonに以下を追加：

{
  "mcpServers": {
    "image-analysis": {
      "command": "node",
      "args": ["/path/to/image-mcp-server/dist/index.js"],
      "env": {
        "OPENAI_API_KEY": "your_openai_api_key"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

Claude Desktop Appの場合

claude_desktop_config.jsonに以下を追加：

{
  "mcpServers": {
    "image-analysis": {
      "command": "node",
      "args": ["/path/to/image-mcp-server/dist/index.js"],
      "env": {
        "OPENAI_API_KEY": "your_openai_api_key"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

使用方法

MCPサーバーが設定されると、以下のツールが利用可能になります：

analyze_image: 画像URLを受け取り、その内容を分析します

使用例

画像URLを分析してください: https://example.com/image.jpg

開発

# 開発モードで実行
npm run dev

ライセンス

ISC