project_plan.md

MCP EdTech Project Plan

Project Overview

This project implements the Model Context Protocol (MCP) for educational technology applications. The implementation will provide a standardized way for EdTech applications to interact with various AI models while maintaining context and state across interactions.

What is Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is a standardized interface for AI model interactions that allows for:

• Consistent handling of context across different models
• Stateful conversations and interactions
• Structured input/output formats
• Model-agnostic implementations
• Enhanced security and privacy controls

Project Architecture

Core Components

1. MCP Core

   • Protocol specification implementation
   • Context management system
   • State persistence layer
   • Model interface adapters

2. EdTech-Specific Extensions

   • Student profile management
   • Learning progress tracking
   • Educational content adaptation
   • Assessment and feedback systems

3. API Layer

   • RESTful endpoints
   • WebSocket support for real-time interactions
   • Authentication and authorization
   • Rate limiting and usage monitoring

4. Demo Applications

   • Interactive tutoring system
   • Personalized learning path generator
   • Knowledge assessment tool

Technical Stack

• Backend: FastAPI
• Database: SQLite (development), PostgreSQL (production)
• Authentication: JWT-based auth
• Documentation: OpenAPI/Swagger, ReDoc
• Testing: Pytest
• Deployment: Docker, Hugging Face Spaces, Heroku/Render compatibility

Implementation Plan

Phase 1: Core MCP Implementation

• Define MCP specification for EdTech use cases
• Implement context management system
• Create model interface adapters
• Develop state persistence layer

Phase 2: EdTech Extensions

• Implement student profile management
• Create learning progress tracking
• Develop educational content adaptation
• Build assessment and feedback systems

Phase 3: API Development

• Design and implement RESTful endpoints
• Add WebSocket support
• Implement authentication and authorization
• Add rate limiting and usage monitoring

Phase 4: Demo Applications

• Build interactive tutoring system
• Create personalized learning path generator
• Develop knowledge assessment tool

Phase 5: Documentation and Deployment

• Write comprehensive documentation
• Create usage examples
• Prepare deployment configurations
• Deploy to Hugging Face and prepare for Heroku/Render

API Endpoints

MCP Core Endpoints

• POST /api/v1/context/create - Create a new context
• GET /api/v1/context/{context_id} - Get context information
• PUT /api/v1/context/{context_id} - Update context
• DELETE /api/v1/context/{context_id} - Delete context
• POST /api/v1/interact - Process an interaction within a context

EdTech-Specific Endpoints

• POST /api/v1/students - Create student profile
• GET /api/v1/students/{student_id} - Get student information
• PUT /api/v1/students/{student_id} - Update student information
• GET /api/v1/students/{student_id}/progress - Get learning progress
• POST /api/v1/assessments - Create assessment
• GET /api/v1/learning-paths/{student_id} - Get personalized learning path

Deployment Strategy

1. Development Environment

   • Local development with SQLite
   • Docker containerization for consistent environments

2. Testing Environment

   • Automated testing with GitHub Actions
   • Integration testing with test databases

3. Production Deployment

   • Hugging Face Spaces for showcase
   • Deployment scripts for Heroku and Render
   • PostgreSQL for production database

Documentation Plan

1. Technical Documentation

   • MCP specification
   • API reference
   • Architecture overview
   • Database schema

2. User Documentation

   • Getting started guide
   • Integration examples
   • Deployment instructions
   • Customization guide

3. Demo Documentation

   • Use case examples
   • Interactive tutorials
   • Sample applications

Timeline

• Phase 1: 1-2 weeks
• Phase 2: 1-2 weeks
• Phase 3: 1 week
• Phase 4: 1-2 weeks
• Phase 5: 1 week

Total estimated time: 5-8 weeks for full implementation