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Mcp Edtech Agent
What is Mcp Edtech Agent
mcp-edtech-agent is an AI-powered assistant integrated with the Sunbird Ed learning platform, utilizing Model Context Protocol (MCP) to provide contextual and conversational support for learners and administrators.
Use cases
Use cases include providing real-time assistance to learners regarding course information, helping administrators manage enrollments and track progress, and enhancing the overall user experience on the Sunbird Ed platform.
How to use
To use mcp-edtech-agent, install the required dependencies via pip, set up environment variables in a .env file, and run the application through the command-line interface.
Key features
Key features include answering user queries about course metadata, simulating Sunbird Ed behavior with mock API responses, providing intelligent responses using LLaMA 3, and laying the foundation for personalized learning support.
Where to use
mcp-edtech-agent can be used in educational technology environments, particularly in platforms focused on personalized learning and skill development.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Mcp Edtech Agent
mcp-edtech-agent is an AI-powered assistant integrated with the Sunbird Ed learning platform, utilizing Model Context Protocol (MCP) to provide contextual and conversational support for learners and administrators.
Use cases
Use cases include providing real-time assistance to learners regarding course information, helping administrators manage enrollments and track progress, and enhancing the overall user experience on the Sunbird Ed platform.
How to use
To use mcp-edtech-agent, install the required dependencies via pip, set up environment variables in a .env file, and run the application through the command-line interface.
Key features
Key features include answering user queries about course metadata, simulating Sunbird Ed behavior with mock API responses, providing intelligent responses using LLaMA 3, and laying the foundation for personalized learning support.
Where to use
mcp-edtech-agent can be used in educational technology environments, particularly in platforms focused on personalized learning and skill development.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
🧠 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) and Sunbird APIs.
✅ Successfully demonstrates API tooling, agent reasoning, and installation-level context using mocked data.
For architecture workflow, please refer to: 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)
- 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
-
Install Requirements
pip install -r requirements.txt -
Set Environment Variables
Create a
.envfile in the root directory: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:
-
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
Add the API key to your.envfile as shown above
Start the assistant: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
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.










