mcp-edtech-agent

# 🧠 Sunbird Ed AI Assistant (MCP Protocol Integration) This project is a prototype AI assistant integrated with the **Sunbird Ed** learning platform using **Model Context Protocol (MCP)** concepts. The goal is to enable contextual, conversational support for learners and admins, powered by **LLaMA 3** (via the [Groq API](https://console.groq.com/)) and Sunbird APIs. > ✅ Successfully demonstrates API tooling, agent reasoning, and installation-level context using mocked data. For architecture workflow, please refer to: [architecture.md](architecture.md) ## 🚀 Project Overview Sunbird Ed is a modular Digital Public Good (DPG) that supports personalized learning and skilling at scale. This project introduces an **AI assistant** that can: - Answer user queries about course metadata, enrollments, and progress. - Simulate Sunbird Ed behavior using mock API responses. - Provide intelligent responses using an LLM (LLaMA 3 via the Groq API) with tool-calling logic. - Lay the foundation for personalized learning support using MCP. ## 🛠 Tech Stack - **Language**: Python - **Model**: [LLaMA 3 (via Groq API)](https://console.groq.com/) - **MCP Protocol**: Integrated conceptually, leveraging Groq and LLaMA's contextual AI capabilities. (Note: No official Python MCP Agent SDK used yet) - **API Simulation**: Local mock APIs (Mockoon) - **Env Management**: `dotenv` - **CLI Interface**: Command-line interface - **IDE**: Pycharm ## 🚧 Updates & Next Steps - **MCP SDK**: MCP SDK is conceptualized and integrated manually via the tool schemas, leveraging Groq's LLaMA 3 model. Future versions may integrate a more formal MCP SDK when available. ## ⚙️ Setup Instructions 1. **Install Requirements** ```bash pip install -r requirements.txt ``` 2. **Set Environment Variables** Create a `.env` file in the root directory: ```env PYTHONPATH=/path/to/sunbird-ai-assistant # Replace with your actual path MCP_MODEL=llama3-8b-8192 # Replace with any other model available in groq MCP_API_BASE=https://api.groq.com/openai/v1 MCP_API_KEY=your_groq_api_key_here # Paste your API key here ``` ## 🧪 Testing Instructions (LLaMA 3 Required) To run the project using LLaMA 3: 1. Start the Assistant: To run the assistant using Groq's hosted LLaMA 3 or other model: Sign up and get an API key from [Groq Console](https://console.groq.com/) Add the API key to your `.env` file as shown above Start the assistant: ```bash python cli/main.py ``` Note: `PYTHONPATH` is automatically appended to `sys.path` during runtime. ## ✅ Current Features - Fetch course listings via `course/v1/search` (mocked) - Retrieve user enrollment data via `user/enrollment/list` (mocked) - Display user profile and progress via `user/v1/profile` (mocked) - Run contextual CLI chat using LLaMA 3 with tool calling logic ## 🌟 Future Improvements - 🔄 Replace mocked endpoints with real Sunbird API integration - 🎯 Add user-level personalization and intelligent guidance - 🧠 Incorporate official Python MCP Agent SDK (once available) - 🌐 Launch a web-based interface (Phase 2) ## 🔐 Requesting API Access Currently using mock APIs. Access to official Sunbird APIs (`course/v1/search`, `user/v1/profile`, `user/enrollment/list`) is requested to enable real-world deployment and validation. ## 📄 License This project is part of **Code4GovTech** and is open-sourced under the **MIT License**. ## 👥 Contributors - **Ganasekhar Kalla** – Developer & MCP Agent Integration - **Sunbird Community** – API & Documentation Support