-Article-Quadrant-Analyzer-MCP-Server

vincent623/-Article-Quadrant-Analyzer-MCP-Server

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The Article Quadrant Analyzer MCP Server is a robust tool designed to extract insights from articles using OCR and generate intelligent Chinese quadrant analysis with direct text matrix visualization.

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📊 Article Quadrant Analyzer MCP Server (Enhanced + OCR)

A powerful Model Context Protocol (MCP) server that extracts core insights from articles with OCR support and generates intelligent Chinese quadrant analysis with direct text matrix visualization.

✨ Features

  • Multi-Source Content Processing: URLs, files, screenshots (OCR), and direct text
  • Professional OCR: Integration with Mistral Document AI API for high-accuracy screenshot analysis
  • 4 Powerful Tools: Content extraction, OCR processing, insights analysis, quadrant generation
  • Chinese Text Matrix Output: Direct ASCII quadrant visualization in dialogue
  • 2x2 Quadrant Analysis: Automatic generation of insightful quadrant visualizations
  • Agent-Centric Design: Optimized for AI agent workflows
  • UVX Deployment: Zero-dependency deployment for minimal cost

🚀 Quick Start

1. Fast Deployment (5 minutes)

# Deploy to Cursor
./deploy_to_ide_standard.sh cursor

# Deploy to VS Code
./deploy_to_ide_standard.sh vscode

# Deploy to Claude Desktop
./deploy_to_ide_standard.sh claude

# Validate deployment
./deploy_to_ide_standard.sh validate

2. Manual Setup

# Install dependencies
uvx --quiet --python 3.12 --with fastmcp python test_simple_server.py

# Start MCP Inspector for testing
fastmcp dev test_simple_server.py

📁 Project Structure

mcp-server-article-quadrant/
├── test_simple_server.py              # Main MCP server (3 tools)
├── deploy_to_ide_standard.sh          # Automated deployment script
├── config/                            # IDE configurations
│   ├── config_cursor_standard.json
│   ├── config_vscode_standard.json
│   ├── config_claude_desktop_standard.json
│   ├── config_emacs.el
│   └── config_neovim.lua
├── src/mcp_server_article_quadrant/   # Modular source code
│   ├── server.py                      # FastMCP server setup
│   ├── tools/                         # MCP tools
│   │   ├── extract_content.py
│   │   ├── analyze_insights.py
│   │   └── generate_quadrant.py
│   ├── models/                        # Pydantic models
│   │   ├── content.py
│   │   ├── analysis.py
│   │   └── quadrant.py
│   └── utils/                         # Utilities
│       ├── content_extractor.py
│       ├── quadrant_generator.py
│       └── image_processor.py
├── .trae/specs/article-quadrant-analyzer/  # Technical specifications
│   ├── spec.md (24KB)                 # Complete MCP server specification
│   └── api-research.md (25KB)         # API research and content sources
├── pyproject.toml                     # Project configuration
├── .env.example                       # Environment variables template
├── 2X2分析prompt.md                   # Original analysis prompt
└── DOCUMENTATION_SUMMARY.md           # Documentation cleanup summary

🔧 Configuration

Environment Variables

# Mistral Document AI API (for OCR)
MISTRAL_API_KEY=your_api_key_here

# Content Processing
CONTENT_MAX_LENGTH=50000
OCR_MAX_FILE_SIZE=10485760

IDE Configuration Examples

Cursor:

{
  "mcpServers": {
    "article-quadrant-analyzer": {
      "command": "uvx",
      "args": [
        "--quiet", "--python", "3.12", "--with", "fastmcp",
        "python", "/Users/vincent/Library/CloudStorage/SynologyDrive-vincent/My.create/Developer/MCP/test_simple_server.py"
      ]
    }
  }
}

More configuration examples in config/ directory.

🛠️ MCP Tools

1. extract_article_content_simple

Enhanced content extraction with AI-friendly interface

Intelligent Processing:

  • Automatic HTML/XML tag removal
  • Language detection (Chinese/English/Mixed)
  • Content quality analysis
  • URL and format detection
  • Comprehensive metrics (characters, words, sentences, paragraphs)

Universal Input Support:

  • URLs (news websites, WeChat public accounts)
  • Text files and documents
  • Direct text input
  • OCR processed content
  • Mixed-format content

Smart Output:

  • Content preview with truncation
  • Complexity assessment
  • Processing recommendations
  • Next-step guidance

2. analyze_article_insights_simple

Advanced content insights extraction

Keyword Analysis:

  • Frequency-based keyword extraction
  • Topic identification and clustering
  • Content summarization
  • Trend detection

Intelligence Features:

  • Automatic topic categorization
  • Insight relevance scoring
  • Content structure analysis
  • Actionable insight generation

3. extract_text_from_image

Professional OCR with Mistral Document AI API

Advanced OCR Processing:

  • High-accuracy text extraction from images and screenshots
  • Support for multiple image formats (PNG, JPG, WEBP)
  • Automatic language detection (Chinese/English/Mixed)
  • Mistral Document AI API integration for best results

Smart Error Handling:

  • Graceful fallback when API key not configured
  • Detailed error messages and troubleshooting guidance
  • Image validation and preprocessing
  • Network timeout and retry logic

Input/Output Support:

  • File paths to local images
  • Base64 encoded image data
  • Real-time confidence scoring
  • Extracted text ready for quadrant analysis

4. generate_quadrant_analysis_simple

Enhanced Chinese quadrant analysis engine

Smart Content Processing:

  • Intelligent Chinese language detection and analysis
  • Context-aware content preprocessing
  • Flexible axis labeling (supports Chinese labels)
  • Robust error handling and parameter validation

Advanced Classification Logic:

  • Collaboration Analysis: Detects team work, coordination, and group activities
  • Textual Analysis: Identifies documentation, writing, and formal communication
  • Pattern Recognition: Maps content to appropriate quadrants based on actual text patterns
  • Chinese Context Support: Specifically trained for Chinese business and work scenarios

Direct Matrix Output:

  • Real-time ASCII Visualization: Matrix appears directly in dialogue
  • Chinese Quadrant Names: 重点投入区, 专业分析区, 基础维护区, 创意协作区
  • Content-Specific Mapping: Analyzes your actual content for accurate placement
  • No Conversion Needed: Instant results without SVG/PNG conversion steps

Rich Output Format:

  • Professional quadrant mapping
  • Detailed content metrics
  • Strategic insights and recommendations
  • Direct text matrix visualization (Chinese)
  • Smart content classification based on actual text analysis

AI-Friendly Features:

  • Automatic XML/HTML tag cleanup
  • Flexible parameter format support
  • Comprehensive error handling
  • Context-aware response generation
  • Chinese language support with intelligent content analysis

🎨 Enhanced Visualization Capabilities:

  • Intelligent Text Matrix: Direct ASCII quadrant display in dialogue
  • Chinese Content Analysis: Smart classification based on collaboration vs text levels
  • Context-Aware Mapping: Analyzes content patterns for accurate quadrant placement
  • Real-time Results: No SVG conversion needed - matrix appears immediately
  • Dynamic Naming: Quadrants named in Chinese (重点投入区, 专业分析区, 基础维护区, 创意协作区)

📋 Supported Content Sources

  • News Websites: Major news platforms and online publications
  • WeChat Public Accounts: Articles from WeChat official accounts
  • Screenshots: OCR processing via Mistral Document AI API
  • Text Files: Direct file content extraction
  • Direct Input: Manual text entry for analysis

🎯 Use Cases

  • Work Process Analysis: Analyze team collaboration workflows and documentation patterns
  • Project Management: Visualize task distribution and work flow efficiency
  • Team Coordination: Identify collaboration bottlenecks and optimization opportunities
  • Content Strategy: Map content types across collaboration and formality dimensions
  • Decision Making: Framework for resource allocation and task prioritization

📊 Sample Output

Input:

工作的流动性: 没有任何一个岗位只存在于一个象限...
例如开发新功能: 团队头脑风暴，撰写PRD文档，工程师独立编写代码...

Direct Matrix Output:

🎯 四象限矩阵图

                    ↑ 文本化程度 ↑
                    ┌──────────────────────────────┐
                    │     Q1: 重点投入区     │
                    │  ┌────────────────────────┐  │
                    │  │ • 团队协作文档      │  │
                    │  │ • 集体讨论记录      │  │
                    │  │ • 共享成果展示      │  │
                    │  └────────────────────────┘  │
                    └──────────────────────────────┘
                    ┌──────────────────────────────┐
                    │     Q2: 专业分析区     │
                    │  ┌────────────────────────┐  │
                    │  │ • 独立深度思考      │  │
                    │  │ • 个人专业分析      │  │
                    │  │ • 核心技术实现      │  │
                    │  └────────────────────────┘  │
                    └──────────────────────────────┘
← 协作程度 ← ─────────────────────────────────── → 协作程度 →
                    ┌──────────────────────────────┐
                    │     Q3: 基础维护区     │
                    │  ┌────────────────────────┐  │
                    │  │ • 基础维护工作      │  │
                    │  │ • 常规操作流程      │  │
                    │  │ • 标准规范执行      │  │
                    │  └────────────────────────┘  │
                    └──────────────────────────────┘
                    ┌──────────────────────────────┐
                    │     Q4: 创意协作区     │
                    │  ┌────────────────────────┐  │
                    │  │ • 创意头脑风暴      │  │
                    │  │ • 视觉化表达        │  │
                    │  │ • 互动协作展示      │  │
                    │  └────────────────────────┘  │
                    └──────────────────────────────┘

🔍 Testing & Validation

# Test MCP Inspector
fastmcp dev test_simple_server.py
# Opens: http://127.0.0.1:6274

# Validate UVX deployment
./deploy_to_ide_standard.sh validate

# Test individual tools via MCP Inspector interface

📚 Documentation

  • - Complete MCP server design (24KB)
  • - Content source analysis (25KB)
  • - Project organization and cleanup history

⚡ Performance

  • Startup Time: <2 seconds with UVX
  • Memory Usage: ~50MB baseline
  • Processing: 1-5 seconds for typical articles
  • OCR Processing: 3-10 seconds via Mistral API

🎨 Generated Output Examples

The server generates professional quadrant analyses in SVG format showing:

  • Strategic Positioning: Content mapped across two axes
  • Visual Clarity: Clean, professional quadrants with labels
  • Actionable Insights: Recommendations based on positioning
  • Contextual Analysis: Tailored to content type and goals

🚀 Ready to transform your article analysis workflow!

Generated with FastMCP Spec-Driven Development Guide