MCP AI Bridge

# MCP AI Bridge - Feature Development Plan ## Current Project Analysis ### Existing Features - **Core AI Integration**: OpenAI (GPT models) and Google Gemini integration - **Security**: Rate limiting, input validation, API key validation, secure error handling - **Logging**: Winston-based structured logging - **Tools**: `ask_openai`, `ask_gemini`, `server_info` - **Configuration**: Flexible env var and config file support ### Current Architecture - Main server: `src/index.js` - Modular components: logger, errors, validators, rateLimiter, constants - Jest testing framework with coverage - MCP SDK integration ## Gemini-Suggested Features Based on consultation with Gemini 1.5 Flash, here are prioritized feature suggestions: ### High Priority Features 1. **Model Chaining/Pipelining** - Chain multiple API calls together - Use output from one model as input to another - Enable complex workflows leveraging different model strengths - **Value**: Sophisticated AI tasks beyond single model capabilities 2. **Response Comparison/Analysis** - Side-by-side comparison of multiple model responses - Highlight similarities and differences - Include metrics like response length, sentiment analysis - **Value**: Help users evaluate and choose best responses 3. **Cost Monitoring and Budgeting** - Track API costs per service and over time - Budget limits and alerts - Cost dashboard/reporting - **Value**: Essential for managing expenses across multiple paid APIs ### Medium Priority Features 4. **Caching Mechanism** - Store and reuse previous API responses for identical prompts - Reduce API calls and latency - **Value**: Improved performance and reduced costs 5. **Customizable Response Formatting** - Output format options (JSON, Markdown, plain text) - Field inclusion/exclusion controls - **Value**: Better integration with other systems ### Lower Priority Features 6. **Prompt Engineering Assistance** - Suggest keywords and prompt improvements - Provide prompt examples and variations - **Value**: Improve AI output quality through better prompts 7. **Plugin System** - Plugin architecture for extensibility - Custom modules and integrations - **Value**: Flexibility for evolving needs ## Implementation Phases ### Phase 1: Foundation Enhancements - [ ] Model chaining/pipelining system - [ ] Basic cost tracking infrastructure - [ ] Response comparison framework ### Phase 2: Advanced Features - [ ] Caching mechanism implementation - [ ] Cost monitoring dashboard - [ ] Response formatting options ### Phase 3: Extensions - [ ] Prompt engineering tools - [ ] Plugin system architecture - [ ] Advanced analytics ## Next Steps 1. Create detailed technical specifications for Phase 1 features 2. Design API interfaces for new tools 3. Plan database/storage requirements for cost tracking and caching 4. Update project architecture to support new features 5. Create implementation roadmap with milestones ## Technical Considerations - Maintain existing security standards - Ensure backward compatibility - Follow existing code patterns and conventions - Comprehensive testing for all new features - Documentation updates for new capabilities