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# 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
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