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    Advanced Code Analysis Mcp

    Advanced Code Analysis MCP Server with Knowledge Graph and AI Insights - TypeScript implementation with Neo4j backend

    1 stars
    TypeScript
    Updated Sep 18, 2025

    Table of Contents

    • ✨ Key Features
    • 🎯 Performance Targets
    • 🚀 Quick Setup
    • System Requirements
    • Setup with Docker
    • Local Installation
    • 📖 Basic Usage
    • Repository Analysis
    • Natural Language Queries
    • Advanced API
    • 🔧 Advanced Configuration
    • Analysis Settings
    • AI Configuration
    • Neo4j Configuration
    • 📊 Monitoring and Metrics
    • 🎨 Usage Examples
    • 1. Enterprise Code Audit
    • 2. Developer Onboarding
    • 3. Refactoring Planning
    • 4. Performance Analysis
    • 🛠️ Development
    • Running Tests
    • Code Quality and Linting
    • Production Build
    • 📚 Additional Documentation
    • 🤝 Contributing
    • 📄 License
    • 🙋‍♂️ Support

    Table of Contents

    • ✨ Key Features
    • 🎯 Performance Targets
    • 🚀 Quick Setup
    • System Requirements
    • Setup with Docker
    • Local Installation
    • 📖 Basic Usage
    • Repository Analysis
    • Natural Language Queries
    • Advanced API
    • 🔧 Advanced Configuration
    • Analysis Settings
    • AI Configuration
    • Neo4j Configuration
    • 📊 Monitoring and Metrics
    • 🎨 Usage Examples
    • 1. Enterprise Code Audit
    • 2. Developer Onboarding
    • 3. Refactoring Planning
    • 4. Performance Analysis
    • 🛠️ Development
    • Running Tests
    • Code Quality and Linting
    • Production Build
    • 📚 Additional Documentation
    • 🤝 Contributing
    • 📄 License
    • 🙋‍♂️ Support

    Documentation

    Advanced Code Analysis MCP Server

    🚀 Advanced code analysis system with knowledge graph and AI insights

    A sophisticated MCP Server that combines Neo4j knowledge graphs, AI-powered analysis, and advanced static analysis for large-scale code projects.

    ✨ Key Features

    • 🧠 AI-Powered Analysis with GPT-4/Claude for advanced insights
    • 📊 Advanced Knowledge Graph with Neo4j for complex relationships
    • 🔍 Natural Language Queries with no learning curve
    • 🏗️ Deep Architectural Analysis
    • ⚡ Parallel Processing for large projects
    • 🛡️ Automated Security Analysis
    • 📈 Comprehensive Quality Metrics

    🎯 Performance Targets

    • Up to 10M lines of code in under 30 minutes
    • 10+ concurrent analyses
    • Query response time < 1 second for most queries
    • 95%+ accuracy in entity and relationship detection

    🚀 Quick Setup

    System Requirements

    • Node.js 18+
    • Docker & Docker Compose
    • 8GB+ RAM
    • 100GB+ disk space

    Setup with Docker

    bash
    # Clone the project
    git clone https://github.com/ItamarZand88/advanced-code-analysis-mcp.git
    cd advanced-code-analysis-mcp
    
    # Setup environment configuration
    cp .env.example .env
    # Edit .env with your settings
    
    # Run the system
    docker-compose up -d
    
    # Check status
    curl http://localhost:3000/health

    Local Installation

    bash
    # Install dependencies
    npm install
    
    # Build the project
    npm run build
    
    # Run in development
    npm run dev
    
    # Run in production
    npm start

    📖 Basic Usage

    Repository Analysis

    typescript
    import { MCPServer } from './dist/index.js';
    
    const server = new MCPServer();
    
    // Start analysis
    const result = await server.analyzeRepository(
      'https://github.com/microsoft/vscode',
      'main',
      {
        includeTests: true,
        enableAIInsights: true,
        parallelWorkers: 8
      }
    );
    
    console.log(`Job ID: ${result.jobId}`);

    Natural Language Queries

    typescript
    // Example queries
    const queries = [
      'Show me the most complex functions in the system',
      'Find circular dependencies',
      'Which files are missing tests?',
      'Show me potential security vulnerabilities',
      'What are the main architectural patterns?'
    ];
    
    for (const query of queries) {
      const result = await server.queryGraph(query, graphId);
      console.log(result.interpretation);
    }

    Advanced API

    typescript
    // Complexity analysis
    const complexity = await server.getComplexityAnalysis(graphId);
    
    // Dependency analysis
    const dependencies = await server.getDependencyAnalysis(graphId);
    
    // Quality assessment
    const quality = await server.getQualityMetrics(graphId);
    
    // Security analysis
    const security = await server.getSecurityAnalysis(graphId);

    🔧 Advanced Configuration

    Analysis Settings

    javascript
    // .env
    MAX_CONCURRENT_ANALYSES=10
    PARALLEL_WORKERS=16
    MAX_FILE_SIZE=50485760  // 50MB
    INCLUDE_TESTS=true
    ENABLE_AI_INSIGHTS=true

    AI Configuration

    javascript
    AI_PROVIDER=openai
    AI_MODEL=gpt-4
    OPENAI_API_KEY=your_key_here
    ANTHROPIC_API_KEY=your_key_here

    Neo4j Configuration

    javascript
    NEO4J_URI=bolt://localhost:7687
    NEO4J_USERNAME=neo4j
    NEO4J_PASSWORD=secure_password
    NEO4J_DATABASE=codeanalysis

    📊 Monitoring and Metrics

    • Neo4j Browser: http://localhost:7474
    • Grafana Dashboard: http://localhost:3001 (admin/admin)
    • Prometheus Metrics: http://localhost:9091
    • Health Check: http://localhost:3000/health

    🎨 Usage Examples

    1. Enterprise Code Audit

    code
    Analyze our main system for security issues and code smells

    2. Developer Onboarding

    code
    Show me the core components and how they interact

    3. Refactoring Planning

    code
    Find the most tightly coupled components and suggest where to break dependencies

    4. Performance Analysis

    code
    Find bottlenecks and inefficient algorithms

    🛠️ Development

    Running Tests

    bash
    npm test
    npm run test:watch
    npm run test:coverage

    Code Quality and Linting

    bash
    npm run lint
    npm run lint:fix
    npm run format

    Production Build

    bash
    npm run build
    npm run docker:build

    📚 Additional Documentation

    • 📖 API Documentation
    • 🏗️ Architecture Guide
    • 🔧 Configuration Reference
    • 🚀 Deployment Guide
    • 🤝 Contributing Guidelines

    🤝 Contributing

    1. Fork the project

    2. Create a feature branch (git checkout -b feature/amazing-feature)

    3. Commit your changes (git commit -m 'Add amazing feature')

    4. Push to the branch (git push origin feature/amazing-feature)

    5. Open a Pull Request

    📄 License

    Distributed under the MIT License. See LICENSE for more information.

    🙋‍♂️ Support

    • 📧 Email: support@code-analysis.com
    • 💬 Discord: Community Server
    • 🐛 Issues: GitHub Issues
    • 📖 Documentation: Full Docs

    ---

    ⭐ Don't forget to star the project if it helps you!

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