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# 🧠 Resume Generator MCP Server
**Generate Ready-to-Send, ATS-Friendly Resumes Instantly**
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## 📖 Table of Contents
- [About the Project](#-about-the-project)
- [Features](#-features)
- [Architecture Overview](#-architecture-overview)
- [Tech Stack](#-tech-stack)
- [Installation](#-installation)
- [Usage Guide](#-usage-guide)
- [MCP Tools Overview](#-mcp-tools-overview)
- [Security & Data Handling](#-security--data-handling)
- [Contributing](#-contributing)
- [Roadmap](#-roadmap)
- [License](#-license)
- [Support](#-support)
- [Credits](#-credits)
---
## 💡 About the Project
**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.
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## ✨ Features
- 🧾 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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## 🧱 Architecture Overview
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> *(A placeholder is left for the diagram you will add later)*
**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
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## 🧰 Tech Stack
**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
---
## ⚙️ Installation
### Prerequisites
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
### Steps
```bash
# 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](https://ngrok.com/)
---
## 🚀 Usage Guide
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:
```bash
POST /generate-resume
{
"input": "I am a software engineer with 5 years of experience in Python and AI..."
}
```
Response:
```json
{
"pdf_link": "https://s3.amazonaws.com/xyz/resume.pdf",
"docx_link": "https://s3.amazonaws.com/xyz/resume.docx"
}
```
---
## 🧩 MCP Tools Overview
**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.
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## 🔒 Security & Data Handling
- 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.
---
## 🧭 Roadmap
- [ ] 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
---
## 🤝 Contributing
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.
---
## 🪪 License
This project is licensed under the **MIT License** — see the [LICENSE](LICENSE) file for details.
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## 🧑💻 Support
For support, questions, or feedback:
- Open an issue on GitHub
- [Email](mailto:abhishekguptacode@gmail.com)
- Follow updates: [LinkedIn](https://linkedin.com/in/iautomates)
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## 🙏 Credits
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!
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⭐ **If you like this project, give it a star! It helps others find it and motivates future development.**
