Hugging Face's logo

Spaces:
Agents-MCP-Hackathon
/
ipmentor
Running

App Files Community
1
ipmentor / README.md
davidlms's picture
davidlms
Update README.md
147ce6e verified
preview code
|
raw
history blame contribute delete
3.05 kB

A newer version of the Gradio SDK is available: 5.35.0

Upgrade
metadata
title: IPMentor
emoji: 🌐
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.33.1
app_file: app.py
pinned: false
license: mit
short_description: IPv4 networking toolkit with verified calculations
tags:
  - Agents-MCP-Hackathon
  - mcp-server-track
  - networking
  - education
  - ipv4
  - subnet-calculator
  - ai-tutoring

IPMentor 🌐

IPMentor is an IPv4 networking toolkit designed as verified computational tools for AI tutoring systems. Built for the Gradio MCP Hackathon 2025, this project demonstrates how MCP can bridge AI tutoring systems with specialized computational tools, creating more reliable and cost-effective educational experiences.

🎯 Hackathon Track: MCP Server/Tool

This Gradio app serves as both an interactive web interface and an MCP Server, providing three core networking tools that AI agents can access through the Model Context Protocol:

  • ip_info - Analyze IPv4 addresses and subnet masks
  • subnet_calculator - Perform subnet calculations with multiple division methods
  • generate_diagram - Create visual network diagrams

🏆 Competing for Mistral AI Choice Award

This project uses Mistral Small 3.1 24B Instruct in the AI chatbot demo, showcasing how smaller, efficient models can handle educational interactions while delegating precise calculations to IPMentor's verified tools.

🎥 Demo Video

Video demonstration: assets/ipmentor-demo.mp4

🤖 Live AI Chatbot Demo

Experience IPMentor in action with an Mistral Small 3.1 24B Instruct: ipmentor-demo

💡 Why IPMentor?

Current AI tutoring faces a challenge: while LLMs can perform calculations, they occasionally make errors and using powerful models for every calculation is expensive. IPMentor solves this by:

  • Verified Calculations: All subnet mathematics uses dedicated algorithms, eliminating computational errors
  • Cost-Effective AI: Smaller models handle pedagogy while IPMentor handles precise calculations
  • Educational Focus: Designed specifically for networking education scenarios

🔗 Links

🛠️ Technical Architecture

Built with Python, Gradio, native IPv4 algorithms, D2 for diagrams, MCP protocol support, and Pydantic validation. This creates a reliable foundation for AI-powered networking education.

Integration Focus: IPMentor is designed to complement LearnMCP-xAPI for comprehensive AI tutoring systems. While IPMentor provides verified computational tools, LearnMCP-xAPI maintains persistent learning records, enabling AI tutors that can both perform accurate calculations and adapt to individual student learning patterns over time.