Personal Resume Agent

A personalized AI agent that reads your resume and provides intelligent responses about your professional background through a standardized MCP (Model Context Protocol) server interface. Built with RAG (Retrieval-Augmented Generation) capabilities to make your professional information queryable through Claude Desktop.

Features

• Resume Processing: Automatically reads and processes resume files (PDF, DOCX, TXT, MD)
• RAG System: Uses ChromaDB and sentence transformers for intelligent content retrieval
• MCP Server: Exposes functionality through standardized MCP protocol
• Skill Matching: Analyzes how well your skills match job requirements
• Natural Language Interface: Ask questions about your experience, skills, education, etc.

Quick Start

1. Install Dependencies

   pip install -r requirements.txt
   

2. Add Your Resume

   # Place your resume files in the data/ directory
   cp your-resume.pdf data/
   

3. Test the Agent

   cd src
   python personal_resume_agent.py
   

4. Run as MCP Server

   cd src
   python mcp_resume_server.py
   

Project Structure

personal-resume-agent/
├── src/                    # Source code
│   ├── resume_rag.py      # RAG system for resume processing
│   ├── personal_resume_agent.py  # Main agent logic
│   └── mcp_resume_server.py      # MCP server implementation
├── data/                   # Resume files storage
├── tests/                  # Test files
├── docs/                   # Documentation
├── examples/               # Usage examples
└── requirements.txt        # Python dependencies

Usage Examples

Direct Agent Usage

from personal_resume_agent import PersonalResumeAgent

agent = PersonalResumeAgent()
await agent.initialize()

# Ask questions about your resume
result = await agent.process_query("What programming languages do I know?")
print(result['response'])

# Analyze skill match for a job
match = await agent.get_skill_match("Python, React, AWS, Docker")
print(f"Match: {match['match_percentage']}%")

MCP Server Tools

The MCP server exposes these tools:

• query_resume: Ask questions about resume content
• get_agent_info: Get agent capabilities and status
• analyze_skill_match: Compare skills with job requirements
• get_resume_summary: Get overview of resume knowledge base

Configuration

Claude Desktop Integration

Add to your Claude Desktop config (claude_desktop_config.json):

{
  "mcpServers": {
    "personal-resume": {
      "command": "python",
      "args": ["/path/to/personal-resume-agent/src/mcp_resume_server.py"],
      "cwd": "/path/to/personal-resume-agent"
    }
  }
}

Supported File Formats

• PDF: Extracted using PyPDF2
• DOCX: Processed with python-docx
• TXT/MD: Plain text files

Requirements

• Python 3.8+
• ChromaDB for vector storage
• Sentence Transformers for embeddings
• PyPDF2 for PDF processing
• python-docx for Word documents

Privacy & Security

🔒 Important Privacy Notes:

• All resume data is processed locally on your machine
• No personal information is sent to external services
• Vector database is stored locally in data/resume_vectordb/
• The data/ directory is excluded from version control
• Never commit personal resume files to public repositories

Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Resume Files  │───▶│   RAG System    │───▶│   MCP Server    │
│   (PDF/DOCX)    │    │  (ChromaDB +    │    │  (Claude Tool)  │
│                 │    │  Transformers)  │    │                 │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                │
                                ▼
                       ┌─────────────────┐
                       │ Personal Resume │
                       │     Agent       │
                       │ (Query Engine)  │
                       └─────────────────┘

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

MIT License - See LICENSE file for details.