Which integrations are available for this server?

Extracts and analyzes content from [WeChat](/mcp/servers/integrations/wechat) public account articles, supporting content extraction, insight analysis, and quadrant visualization.

📊 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

Technical Specification - Complete MCP server design (24KB)
API Research - Content source analysis (25KB)
Documentation Summary - 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

Article Quadrant Analyzer MCP Server

📊 Article Quadrant Analyzer MCP Server (Enhanced + OCR)

✨ Features

🚀 Quick Start

1. Fast Deployment (5 minutes)

2. Manual Setup

📁 Project Structure

🔧 Configuration

Environment Variables

IDE Configuration Examples

🛠️ MCP Tools

1. `extract_article_content_simple`

2. `analyze_article_insights_simple`

3. `extract_text_from_image`

4. `generate_quadrant_analysis_simple`

📋 Supported Content Sources

🎯 Use Cases

📊 Sample Output

🔍 Testing & Validation

📚 Documentation

⚡ Performance

🎨 Generated Output Examples

Resources

New MCP Servers

Latest Blog Posts

MCP directory API

📊 Article Quadrant Analyzer MCP Server (Enhanced + OCR)

✨ Features

🚀 Quick Start

1. Fast Deployment (5 minutes)

2. Manual Setup

📁 Project Structure

🔧 Configuration

Environment Variables

IDE Configuration Examples

🛠️ MCP Tools

1. extract_article_content_simple

2. analyze_article_insights_simple

3. extract_text_from_image

4. generate_quadrant_analysis_simple

📋 Supported Content Sources

🎯 Use Cases

📊 Sample Output

🔍 Testing & Validation

📚 Documentation

⚡ Performance

🎨 Generated Output Examples

Resources

New MCP Servers

Latest Blog Posts

MCP directory API

1. `extract_article_content_simple`

2. `analyze_article_insights_simple`

3. `extract_text_from_image`

4. `generate_quadrant_analysis_simple`