Overview
A comprehensive secure task management platform consisting of three integrated components: a main application, Python SDK, and MCP server integration. This suite demonstrates modern security practices including OAuth 2.0 implementation, secure session management, and API security patterns.
Security Architecture
Core Security Components
- OAuth 2.0 Authorization Server: RFC-compliant implementation with PKCE, refresh tokens, and scoped access control
- Multi-layer Authentication: Session-based web auth + OAuth bearer tokens for API access
- Secure Password Management: bcryptjs hashing with salt rounds and secure session cookies
- API Security: Comprehensive input validation, parameterized queries, and rate limiting considerations
TaskManager Core Application
Built with Astro and PostgreSQL, featuring:
- Session Security: HTTP-only cookies with 7-day expiration and SameSite protection
- Database Security: Parameterized queries preventing SQL injection
- OAuth Integration: Full authorization server supporting third-party integrations
- Security Testing: Comprehensive test suite including security-focused API tests
Python SDK
Type-safe Python library providing:
- Secure Authentication Flow: Automated OAuth client setup and token management
- Exception Safety: Comprehensive error handling for security failures
- API Coverage: Complete abstraction over all TaskManager endpoints
- Development Security: Built-in validation and secure defaults
MCP Server Integration
Model Context Protocol server with:
- OAuth-protected MCP Tools: Secure AI agent access to task management functionality
- Token Introspection: RFC 7662 compliant token validation
- Resource Server Pattern: Proper separation of authorization and resource concerns
- API Security: Comprehensive request validation and error handling
AI Security Relevance
This suite addresses several AI security concerns:
- Secure AI Agent Integration: MCP server provides controlled, OAuth-protected access for AI agents
- API Security Patterns: Demonstrates proper authentication/authorization for AI service integrations
- Data Access Control: Fine-grained permissions system suitable for AI applications handling sensitive task data
- Audit Trail: Comprehensive logging suitable for AI system monitoring and compliance
Technical Innovation
The project showcases modern security practices including dual authentication modes, comprehensive OAuth 2.0 implementation, and secure AI agent integration patterns that are increasingly important in AI-powered applications.