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| 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](assets/ipmentor-demo.mp4) | |
| ## 🤖 Live AI Chatbot Demo | |
| Experience IPMentor in action with an Mistral Small 3.1 24B Instruct: [ipmentor-demo](https://huggingface.co/spaces/Agents-MCP-Hackathon/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 | |
| - **GitHub Repository**: [https://github.com/DavidLMS/ipmentor](https://github.com/DavidLMS/ipmentor) | |
| - **AI Chatbot Demo**: [https://huggingface.co/spaces/Agents-MCP-Hackathon/ipmentor-demo](https://huggingface.co/spaces/Agents-MCP-Hackathon/ipmentor-demo) | |
| ## 🛠️ 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](https://github.com/DavidLMS/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. |