READMEmain

README.md

Resume Analyzer + LinkedIn/Naukri Job Fetcher + MCP Server

This project allows you to:

Upload a Resume (PDF)
Analyze Resume Summary, Skill Gaps, and Future Roadmap using EURI AI
Auto-fetch matching jobs from LinkedIn and Naukri using Apify
Wrap the job fetch functions into a FastMCP Server for integration with external tools like Claude Desktop, MCP Inspector, etc.

💪 Full Setup Instructions

1. Clone the Repository

git clone <your-repo-link>
cd resume_job_fetcher_project

2. Create Conda Virtual Environment (Recommended)

conda create -n resume_fetcher_env python=3.10
conda activate resume_fetcher_env

3. Install Required Packages

Using pip:

pip install -r requirements.txt

requirements.txt contains:

streamlit
pymupdf
euriai
python-dotenv
apify-client
fastmcp

Alternatively, using UV:

Install UV on Windows Powershell:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Install requirements:

uv pip install -r requirements.txt

4. Configure Environment Variables

Create a .env file:

EURI_API_KEY=your_real_euri_api_key_here
APIFY_API_TOKEN=your_real_apify_api_token_here

5. Run the Streamlit App

streamlit run app.py

6. Run MCP Server (FastMCP)

python mcp_server.py

7. Test MCP Server (Optional)

Install MCP Inspector:

npm install -g @modelcontextprotocol/inspector

Run the Inspector:

npx @modelcontextprotocol/inspector python mcp_server.py

🚀 UV-Based Project Setup (Optional)

uv init mcp-server-demo
cd mcp-server-demo
uv add "mcp[cli]"

To manually install MCP CLI:

pip install "mcp[cli]"

Running standalone MCP locally:

uv run mcp

📆 Project Structure

resume_job_fetcher_project/
├── app.py          # Streamlit frontend app
├── mcp_server.py   # FastMCP server exposing job fetchers
├── requirements.txt
├── README.md
└── .env            # API keys configuration

🌟 Technology Stack

Component	Technology
Resume Analysis	EURI AI (GPT-4.1-nano)
Job Fetching	Apify (LinkedIn + Naukri Actors)
Frontend	Streamlit
MCP Server	FastMCP
Environment Management	Conda / UV
Inspector Tool	MCP Inspector

📃 Important Links

🔫 Quick Commands Summary

Task	Command
Create Conda Env	`conda create -n resume_fetcher_env python=3.10`
Activate Env	`conda activate resume_fetcher_env`
Install Packages	`pip install -r requirements.txt` or `uv pip install -r requirements.txt`
Run App	`streamlit run app.py`
Run MCP Server	`python mcp_server.py`
Launch MCP Inspector	`npx @modelcontextprotocol/inspector python mcp_server.py`

💪 Conclusion

Resume Analyzer + Live Job Fetcher using AI 🚀
Fully ready MCP tool server integration
Modular, scalable, and professional setup

🚀 Let's Build Smarter Applications Faster with AI, Apify, MCP & Streamlit!

Resume-Analyzer-Agent