Article Quadrant Analyzer MCP Server

# 📊 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) ```bash # 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 ```bash # 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 ```bash # 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:** ```json { "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 ```bash # 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 - **[Technical Specification](.trae/specs/article-quadrant-analyzer/spec.md)** - Complete MCP server design (24KB) - **[API Research](.trae/specs/article-quadrant-analyzer/api-research.md)** - Content source analysis (25KB) - **[Documentation Summary](DOCUMENTATION_SUMMARY.md)** - 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*