What is Resume Analyzer Agent

Resume-Analyzer-Agent is an MCP Server project designed to analyze resumes and fetch job listings from LinkedIn and Naukri. It utilizes EURI AI for resume analysis and Apify for job fetching.

Use cases

Use cases include analyzing resumes for skill gaps, providing personalized job recommendations, and integrating with external tools for enhanced functionality.

How to use

To use Resume-Analyzer-Agent, clone the repository, set up a Conda virtual environment, install the required packages, configure environment variables, and run the Streamlit app followed by the MCP Server.

Key features

Key features include resume upload and analysis, skill gap identification, future roadmap suggestions, and automatic job fetching from LinkedIn and Naukri.

Where to use

Resume-Analyzer-Agent can be used in recruitment agencies, job portals, and by individual job seekers looking to enhance their resume and find suitable job opportunities.

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

📃 Important Links

• EURI API Documentation
• Apify API Documentation
• FastMCP GitHub
• Streamlit Documentation
• UV Installation Guide
• MCP Inspector GitHub

🔫 Quick Commands Summary

💪 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!