🎯 Resume Optimizer MCP Server

> Model Context Protocol server for AI-powered resume optimization

> A working prototype demonstrating MCP architecture with Claude integration

🎥 What This Demonstrates

This is a working prototype that showcases:

• ✅ MCP Server implementation - Custom tools using Model Context Protocol
• ✅ Claude integration - Works as MCP client for orchestration
• ✅ Tool composition - Multiple tools working together
• ✅ Real-world use case - Resume optimization workflow

Status: Core architecture implemented, production features planned (see Roadmap)

🏗️ Current Architecture

What's Implemented ✅

┌─────────────────────────────────────┐
│         Claude (MCP Client)         │  ← Orchestration
└──────────────┬──────────────────────┘
               │ MCP Protocol
┌──────────────▼──────────────────────┐
│       MCP Server (Node.js)          │
│  ┌─────────────────────────────┐   │
│  │  Tools Layer:               │   │
│  │  • fetch-data.js            │   │  ← Implemented
│  │  • heap-filter.js           │   │
│  │  • heap.js                  │   │
│  └─────────────────────────────┘   │
└─────────────────────────────────────┘

Production Architecture (Designed) 📋

I've designed a full production system that extends this prototype:

User → MCP Server → Claude → Database Layer (Vector DB, Neo4j, Redis)
                              ↓
                        RAG Pipeline → Optimized Resume

Full architecture documentation: docs/ARCHITECTURE_PLAN.md

💡 The "Master Move": Why MCP?

I chose MCP Server over alternatives (LangChain, custom API) because:

Evaluated Approaches:

Key advantages:

• 🔧 Standardized protocol - Works with any MCP client
• 🧪 Testability - Tools can be unit tested independently
• 🎯 Separation of concerns - Business logic isolated from AI orchestration
• 📦 Composability - Easy to add new tools

Trade-offs I accepted:

• 2-week learning curve (MCP is new)
• Emerging ecosystem (fewer examples)
• Worth it for maintainability and standardization

See full analysis: docs/TRADEOFFS.md

🛠️ Tech Stack

Currently Implemented ✅

• Node.js 18+ - Server runtime
• MCP Protocol - Tool standardization
• Claude API - LLM orchestration via MCP client
• JavaScript - Core implementation

Planned for Production 📋

• Vector Database (Pinecone/FAISS) - For semantic search
• Neo4j - Skill relationship mapping
• Redis - Caching layer
• Docker - Containerization
• GCP Cloud Run - Serverless deployment
• GitHub Actions - CI/CD pipeline

🚀 Quick Start

Prerequisites

• Node.js 18 or higher
• Anthropic API key (for Claude)

Installation

# Clone the repository
git clone https://github.com/YOUR_USERNAME/resume-optimizer-mcp.git
cd resume-optimizer-mcp

# Install dependencies
npm install

# Set environment variables
cp .env.example .env
# Edit .env and add your ANTHROPIC_API_KEY

# Start the MCP server
node server.js

Using with Claude Desktop

1. Update your Claude Desktop MCP config:

{
  "mcpServers": {
    "resume-optimizer": {
      "command": "node",
      "args": ["/path/to/resume-optimizer-mcp/server.js"]
    }
  }
}

2. Restart Claude Desktop

3. The tools will be available in Claude!

🧪 Current Features

Implemented Tools ✅

1. `fetch-data`

Fetches and processes resume/job description data

Usage in Claude:

"Can you fetch the job description data for this role?"

2. `heap-filter`

Filters and prioritizes information using heap data structure

Usage in Claude:

"Filter the top skills from this data set"

3. `heap`

Core heap implementation for priority-based processing

📖 Documentation

Core Documents

• **TRADEOFFS.md** 💎 - Why I made each architectural decision
• **ARCHITECTURE_PLAN.md** - Full production system design
• **MCP_TOOLS.md** - Tool specifications and examples

Key Insights

From my architecture analysis:

1. MCP vs LangChain: Chose protocol standardization over framework convenience

2. Node.js vs Python: Chose Node.js for async I/O performance in tool layer

3. Modular tools: Each tool is independently testable and reusable

🗺️ Roadmap

Phase 1: Prototype ✅ (Current)

• [x] MCP server implementation
• [x] Basic tool structure
• [x] Claude integration
• [x] Architecture documentation

Phase 2: Core Features 🚧 (Next)

• [ ] Complete 6-segment workflow
  • [ ] Job description parser
  • [ ] Market intelligence analyzer
  • [ ] ATS scanner
  • [ ] Skill gap detector
  • [ ] Learning path generator
  • [ ] Resume optimizer
• [ ] Unit tests for all tools
• [ ] Integration tests

Phase 3: Data Layer 📋 (Planned)

• [ ] Vector database integration (FAISS)
• [ ] Graph database for skills (Neo4j)
• [ ] Caching layer (Redis)
• [ ] RAG pipeline implementation

Phase 4: Production 📋 (Planned)

• [ ] Docker containerization
• [ ] CI/CD with GitHub Actions
• [ ] GCP Cloud Run deployment
• [ ] Monitoring and observability
• [ ] Security hardening

🎓 What I Learned

Technical Skills

• MCP Protocol - Deep understanding of standardized AI tool interfaces
• Claude Integration - Working with Claude as an MCP client
• Tool Design - Creating composable, testable tools
• Architecture Thinking - Designing systems that scale

Design Principles

1. Start with protocol, not framework - Standards outlive frameworks

2. Design for testability - Separation of concerns enables unit testing

3. Document trade-offs - Every decision has pros/cons

4. Plan for production - Prototype with production architecture in mind

Key Insights

From TRADEOFFS.md:

> "I chose MCP Server over LangChain because protocol standardization enables tool reusability across any MCP client. The 2-week learning curve was worth it for long-term maintainability. This is the difference between building a demo and architecting a system."

🧠 Architecture Highlights

Why This Matters for Production

This prototype demonstrates:

• ✅ Understanding of tool abstraction
• ✅ Protocol-first thinking (not framework-dependent)
• ✅ Separation of concerns
• ✅ Foundation for scaling

Next steps to production:

1. Add data persistence layer

2. Implement RAG pipeline

3. Add authentication

4. Deploy to cloud

5. Set up CI/CD

Estimated time to production: 4-6 weeks

Detailed plan: docs/PRODUCTION_PLAN.md

🤝 Contributing

This is currently a prototype/learning project. Feedback and suggestions welcome!

Areas I'm exploring:

• Best practices for MCP tool design
• RAG pipeline architecture
• Production deployment strategies
• Cost optimization techniques

📝 Project Status

Current State: Working prototype demonstrating MCP architecture

Lines of Code: ~500

Tools Implemented: 3

Test Coverage: TBD (next phase)

What's Working:

• ✅ MCP server runs and registers tools
• ✅ Claude can discover and use tools
• ✅ Basic tool functionality implemented

What's Next:

• 🚧 Complete all 6 workflow segments
• 🚧 Add database integrations
• 🚧 Implement testing framework
• 🚧 Production deployment

📧 Contact

Krithika Rajendran

• 📧 Email: rkrithika1993@gmail.com
• 💼 LinkedIn: linkedin.com/in/krithika-rajendran
• 🐱 GitHub: github.com/krithika93

🎯 For Hiring Managers

What this project demonstrates:

1. Architectural thinking - Designed full production system (see TRADEOFFS.md)

2. Protocol understanding - Chose MCP for standardization, not convenience

3. Pragmatic approach - Built working prototype, documented full vision

4. Production mindset - Thought through scaling, testing, deployment

Questions I can discuss:

• Why MCP over LangChain?
• How would you scale this to 100K users?
• What are the trade-offs of RAG vs fine-tuning?
• How would you test MCP tools?

What I'm eager to learn:

• Production GenAI patterns at scale
• Real-world RAG challenges
• Cost optimization techniques
• AI safety and governance

📊 Project Stats

• Started: October 2024
• Status: Active prototype
• Primary Focus: Learning MCP protocol and production AI architecture
• Next Milestone: Complete 6-tool workflow