MCP Resume Scorer with Leaderboard
Implements FastAPI endpoints with robust error handling for resume scoring functionality
Uses Flask framework for OAuth token handling and web application functionality
Provides Google OAuth integration for secure authentication and access to Google services
Integrates with Google Cloud Console for OAuth credentials management
Enables public access and webhook testing through ngrok tunneling for local development
Provides Swagger UI for easy API testing and documentation of resume scoring endpoints
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Resume Scorer with Leaderboardscore my resume and show where I rank on the leaderboard"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Resume Scorer with Leaderboard
Welcome to the MCPs repository! This project showcases custom MCP server implementations designed to provide AI models with secure, contextual access to tools and data. Whether you're building resume scoring endpoints, integrating OAuth, or experimenting with FastAPI and Gemini, this repo is your playground for creative, real-world AI utilities.
๐ Features
โ Resume scoring with AI-based feedback
๐ Google OAuth integration for secure access
โก FastAPI endpoints with robust error handling
๐งช Swagger UI for easy testing and documentation
๐ ngrok tunneling for public access and webhook testing
Related MCP server: Enhanced MCP Server
๐ Project Structure
mcp-starter-main/
โโโ mcp-bearer-token/
โ โโโ app.py # Flask app with OAuth and token handling
โ โโโ credentials.json # Google OAuth secrets (not committed)
โ โโโ requirements.txt # Python dependencies
โ โโโ README.md # You're reading it!๐ง Setup Instructions
1. Clone the repo
git clone https://github.com/Mokksh-bhatt/MCPs.git
cd MCPs/mcp-bearer-token2. Create a virtual environment
python -m venv venv
.\venv\Scripts\activate # Windows3. Install dependencies
pip install -r requirements.txt4. Add your Google OAuth credentials
Download
credentials.jsonfrom Google Cloud ConsolePlace it in the project root
If named differently, set the environment variable:
$env:GOOGLE_CLIENT_SECRETS = "your_file_name.json"5. Run the server
python app.py๐ Expose Locally with ngrok
ngrok http 5000Visit http://127.0.0.1:4040 for the ngrok dashboard and copy your public URL.
๐ง Future Plans
Add leaderboard scoring and resume feedback visualization
Integrate Gemini fallback models
Deploy to cloud platforms for persistent access
๐ค Contributing
Pull requests, ideas, and feedback are welcome! Feel free to fork and build your own MCP extensions.
๐ License
This project is open-source under the MIT License.
โจ Author
Built with curiosity and creativity by Mokksh Bhatt
This server cannot be deployed
Maintenance
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Akshay Shetty's engineering portfolio and resume, queryable by AI. OAuth-secured.
OAuth Multi-LLM Orchestrator
Browse, search and scan a public leaderboard of AI-generated GitHub repos.
Persistent memory and vector search for AI agents. Hosted, OAuth-protected via Google sign-in.
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