Generate Ready-to-Send, ATS-Friendly Resumes Instantly

• About the Project
• Features
• Architecture Overview
• Tech Stack
• Installation
• Usage Guide
• MCP Tools Overview
• Security & Data Handling
• Contributing
• Roadmap
• License
• Support
• Credits

Resume Generator MCP Server is a powerful, AI-driven backend server that automatically generates ATS-friendly, professional resumes from any input — whether it’s a text, file, or LinkedIn profile.

It uses FastMCP for the Model Context Protocol layer and integrates OpenAI, Meta LLama, LangChain, and other cloud tools to produce high-quality Word and PDF resumes — instantly uploaded to AWS with secure download links.

• 🧾 Generate resumes from raw text, existing resume, or LinkedIn profile
• 📄 Enhance and tailor resumes for specific job descriptions
• 🧠 Uses LLM intelligence (OpenAI + LLama) for resume optimization
• ☁️ Cloud-powered (AWS S3, Textract, CloudConvert, Neon DB)
• 🔒 Secure resume storage (auto-deletes after 7 days)
• 🔁 Supports multiple templates dynamically rendered via docxtpl + Jinja2
• ⚙️ Modular, scalable, and fully documented MCP server
• 🌐 Deployed with CI/CD via GitHub Actions on Render & Vercel

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High-Level Flow:

1. Choose a predefined resume template
2. Provide your input (LinkedIn URL, text, or file)
3. AI extracts, structures, and enhances your information
4. MCP Server generates Word + PDF resumes
5. You instantly receive secure download links

Core Technologies

• FastMCP
• Python (LangChain, docxtpl, Jinja2)
• AWS (S3, Textract, IAM)
• PostgreSQL (Neon)
• CloudConvert, ScrapingDog APIs
• OpenAI + Meta LLama
• ReactJS (Documentation & Demo UI)
• Alembic (DB Migration)
• Stytch Authentication
• CI/CD with GitHub Actions + Render Deployment

Ensure you have the following installed:

• Python 3.10+
• PostgreSQL (Neon DB connection string)
• AWS Account with S3, IAM, and Textract access
• CloudConvert & ScrapingDog API keys
• Stytch Authentication credentials
• OpenAI and Meta API keys

# Clone the repo
git clone https://github.com/1abhi6/Resume-Generator-MCP-Server.git

# Navigate to project directory
cd Resume-Generator-MCP-Server

# uv setup
uv init
uv venv
.venv/Scripts/activate

# Install dependencies
uv sync

# Set environment variables
cp .env.example .env
# Update .env with your credentials

# Run the MCP Server
uv run main.py

We recommend using ngrok

Once the server is running locally or deployed:

1. Choose an input mode:

   • Raw text
   • Resume file (PDF/DOCX/Image)
   • LinkedIn profile URL

2. Call the corresponding API endpoint or MCP client tool.

3. Receive instant Word & PDF download links.

Example API usage:

POST /generate-resume
{
  "input": "I am a software engineer with 5 years of experience in Python and AI..."
}

Response:

{
  "pdf_link": "https://s3.amazonaws.com/xyz/resume.pdf",
  "docx_link": "https://s3.amazonaws.com/xyz/resume.docx"
}

Primary Tools

1. Generate Resume from Raw Text
2. Enhance Existing Resume
3. Generate Resume based on Job Description
4. Generate Resume from LinkedIn Profile

Utility Tools

• Check Server Health
• Upload Resume File
• Check Uploaded File

Each tool returns Word + PDF download links of the generated resume.

• All resumes are stored in AWS S3 for 7 days only (auto-expiry).
• Input/output guardrails prevent processing of harmful or sensitive data.
• OAuth implemented via Stytch Authentication.
• Secure role-based IAM policies for AWS resources.

• Add more customizable templates
• Introduce multi-language support
• Add analytics dashboard for resume performance
• Enable user accounts with history tracking
• Integrate with more LLM providers

Contributions are welcome!
Please follow these steps:

1. Fork the repository
2. Create your 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

Ensure your code follows proper linting and modular design.

This project is licensed under the MIT License — see the LICENSE file for details.

For support, questions, or feedback:

• Open an issue on GitHub
• Email
• Follow updates: LinkedIn

Special thanks to:

• OpenAI & Meta for LLMs
• AWS, Stytch, and Neon for cloud infrastructure
• FastMCP for protocol design
• All open-source contributors helping make this project better!

⭐ If you like this project, give it a star! It helps others find it and motivates future development.