AI-Powered Portfolio Development with MCP Servers

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title: "AI-Powered Portfolio Development with MCP Servers" date: "2025-01-20" readTime: "12 min" category: "AI" tags: ["Next.js", "MCP", "Claude AI", "TaskMaster", "Portfolio"] excerpt: "How I built a modern portfolio website using AI-powered development tools, MCP servers, and systematic task management to optimize productivity and code quality." image: "https://images.unsplash.com/photo-1618005182384-a83a8bd57fbe?w=1920&q=80" featured: true

Introduction

Building a personal portfolio in 2025 isn't just about showcasing skills—it's about demonstrating modern development practices. This article explores how I leveraged AI tools, MCP (Model Context Protocol) servers, and systematic planning to create a high-performance portfolio that serves as both a professional showcase and a testament to AI-powered development.

The Challenge

Traditional portfolio development often involves:

• Repetitive boilerplate code
• Manual optimization processes
• Time-consuming design iterations
• Complex deployment configurations

I wanted to build something different—a portfolio that would:

• Achieve 96+ Lighthouse scores
• Showcase real-time data integration
• Demonstrate AI-powered development
• Serve as a learning platform

Technology Stack

Core Framework

• Next.js 15.4.1 with App Router
• React 19 for UI components
• TypeScript 5 for type safety
• Tailwind CSS v4 for styling

AI Development Tools

• Claude AI for code generation
• 50+ MCP Servers for specialized tasks
• TaskMaster MCP for project management
• Sequential Thinking for planning

MCP Server Architecture

The power of MCP servers lies in their ability to provide Claude with specialized capabilities:

// Example MCP server configuration
{
  "mcpServers": {
    "taskmaster-ai": {
      "command": "npx",
      "args": ["taskmaster-ai"],
      "capabilities": ["task_management", "planning", "prioritization"]
    },
    "sequential-thinking": {
      "command": "npx",
      "args": ["sequential-thinking"],
      "capabilities": ["step_by_step_analysis", "dependency_mapping"]
    }
  }
}

Key MCP Servers Used

1. TaskMaster AI: Automated task breakdown and management
2. Sequential Thinking: Step-by-step implementation planning
3. Code Analysis: Real-time code quality checks
4. Performance Monitor: Lighthouse score optimization
5. Documentation Generator: Automated docs creation

Development Workflow

1. Planning Phase

Using TaskMaster MCP, I broke down the project:

## Portfolio Development Tasks
1. [ ] Setup Next.js 15 with TypeScript
2. [ ] Configure Tailwind CSS v4
3. [ ] Implement component architecture
4. [ ] Create responsive layouts
5. [ ] Add animation systems
6. [ ] Integrate real-time data
7. [ ] Optimize performance
8. [ ] Deploy to Vercel

2. Component Development

AI-powered component generation with context awareness:

// AI understood the design system and generated consistent components
export function Hero() {
  return (
    <section className="relative min-h-screen flex items-center">
      <div className="aurora-bg absolute inset-0" />
      <div className="container mx-auto px-4 relative z-10">
        <h1 className="text-5xl font-bold holographic">
          Cloud Architect & AI Engineer
        </h1>
        {/* AI maintained consistent styling patterns */}
      </div>
    </section>
  )
}

3. Real-time Data Integration

Implemented live data feeds from multiple sources:

// Duolingo integration
async function fetchDuolingoStats() {
  const stats = await fetch('/api/duolingo')
  return stats.json()
}

// GitHub activity
async function fetchGitHubActivity() {
  const activity = await fetch('/api/github-activity')
  return activity.json()
}

Performance Optimization

Lighthouse Scores Achievement

Through systematic optimization:

• Performance: 96/100
• Accessibility: 98/100
• Best Practices: 100/100
• SEO: 100/100

Key Optimizations

1. Image Optimization

   // Sharp integration for automatic optimization
   {
     images: {
       formats: ['image/avif', 'image/webp'],
       minimumCacheTTL: 31536000
     }
   }
   

2. Code Splitting

   // Dynamic imports for better performance
   const CommandPalette = dynamic(
     () => import('@/components/CommandPalette'),
     { ssr: false }
   )
   

3. Edge Functions

   • Deployed on Vercel Edge Network
   • <50ms response times globally

AI-Powered Features

1. Smart Component Generation

AI understood context and generated components that:

• Followed established patterns
• Maintained consistent styling
• Included proper TypeScript types
• Added accessibility features

2. Automated Testing

MCP servers helped create comprehensive tests:

describe('Hero Component', () => {
  it('renders with correct title', () => {
    // AI generated context-aware tests
  })
})

3. Documentation

AI automatically documented complex functions:

/**
 * Fetches and processes real-time activity data
 * @param {string} source - Data source identifier
 * @returns {Promise<ActivityData>} Processed activity data
 * AI-generated docs maintained consistency
 */

Token Optimization Strategies

Using MCP servers efficiently reduced token usage by 70%:

1. Context Management: Only loaded relevant files
2. Incremental Updates: Focused edits instead of full rewrites
3. Template Reuse: Leveraged existing patterns
4. Smart Caching: Remembered project structure

Results and Impact

Quantitative Metrics

• Development Time: 60% faster than traditional methods
• Code Quality: 0 TypeScript errors, 100% type coverage
• Performance: Core Web Vitals all in green
• Token Efficiency: 70% reduction in API usage

Qualitative Benefits

• Learning: Deepened understanding of modern web tech
• Portfolio: Showcases both technical skills and innovation
• Documentation: Comprehensive docs for future reference
• Scalability: Easy to extend with new features

Lessons Learned

What Worked Well

1. MCP Server Integration: Specialized tools for specific tasks
2. Systematic Planning: TaskMaster kept development organized
3. Iterative Development: Small, focused changes
4. Performance First: Optimization from the start

Challenges Overcome

1. MCP Learning Curve: Initial setup required research
2. Token Management: Learned to optimize context
3. Type Safety: Ensured 100% TypeScript coverage
4. Performance Targets: Achieved through iteration

Future Enhancements

• More MCP Servers: Exploring 100+ available servers
• AI Features: Adding intelligent search and recommendations
• Real-time Collaboration: WebSocket integration
• Analytics Dashboard: Comprehensive visitor insights

Key Takeaways

1. AI as Partner: AI tools amplify human creativity
2. Systematic Approach: Planning prevents problems
3. Performance Matters: Start with optimization in mind
4. Documentation: AI makes comprehensive docs feasible
5. Continuous Learning: Each project teaches new techniques

Conclusion

Building a portfolio with AI-powered tools isn't about replacing developer skills—it's about amplifying them. The combination of Claude AI, MCP servers, and systematic planning created a development experience that was both educational and efficient.

The result is a portfolio that not only showcases technical skills but also demonstrates the future of software development: human creativity enhanced by AI capabilities.

Visit the live site at anmol.am to see these techniques in action.