Mcp Resume Screening

Abstract

1. Introduction

A Model Context Protocol (MCP) server that provides intelligent job matching capabilities. Extract structured job requirements from job descriptions and find/rank candidates from your LlamaCloud resume index.

2. Objective

Codebase to top candidates - a remote MCP server for finding most qualified engineer candidates without leaving your IDE

This project demonstrates how Python can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/run-llama/mcp_resume_screening.git
cd mcp_resume_screening

1. Authentication errors: Ensure Cloud Run proxy is running and you have proper IAM roles
2. Build failures: Check Dockerfile and dependencies in pyproject.toml
3. Port conflicts: Use lsof -ti:8080 | xargs kill -9 to free up port 8080

# Install using uv (recommended)
uv install

# Or using pip
pip install fastmcp httpx

python server.py

# Build image
docker build -t job-matching-mcp .

# Run container
docker run -p 8080:8080 \
  -e OPENAI_API_KEY="your-key" \
  -e LLAMA_CLOUD_API_KEY="your-key" \
  job-matching-mcp

# Build and push to Artifact Registry
gcloud builds submit --region=us-central1 \
  --tag us-central1-docker.pkg.dev/$PROJECT_ID/remote-mcp-servers/mcp-server:latest

# Deploy to Cloud Run
gcloud run deploy mcp-server \
  --image us-central1-docker.pkg.dev/$PROJECT_ID/remote-mcp-servers/mcp-server:latest \
  --region=us-central1 \
  --no-allow-unauthenticated \
  --set-env-vars OPENAI_API_KEY="your-key",LLAMA_CLOUD_API_KEY="your-key"

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by run-llama and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.