MCP Sigmund

# MCP Sigmund Roadmap This document outlines the strategic development plan for MCP Sigmund, focusing on privacy, security, and enhanced functionality. ## ⚠️ IMPORTANT LEGAL DISCLAIMER **MCP Sigmund is an educational learning resource and data analysis tool, NOT a financial advisor or advisory service.** ### 🚫 **NOT FINANCIAL ADVICE** - This system does **NOT** provide financial advice, recommendations, or guidance - All insights, analysis, and suggestions are for **educational purposes only** - Users must make their own financial decisions based on their own research and judgment - No information from this system should be considered as investment, tax, or financial advice ### 📚 **Educational Purpose Only** - MCP Sigmund is designed as a **learning resource** for understanding personal financial data - The system helps users analyze and understand their financial patterns and trends - All outputs are intended for **educational and informational purposes** - Users should consult qualified financial professionals for actual financial advice **By using MCP Sigmund, you acknowledge this is an educational tool, not a financial advisory service.** ## 🎯 Vision Transform MCP Sigmund into a privacy-first, locally-deployable financial data server that enables secure AI interactions with personal financial data while maintaining complete user control and data sovereignty. ## 📋 Current Status (v1.0.0) ✅ **Completed Features:** - PostgreSQL database integration with multi-provider support - Smart formatting with context detection - Comprehensive error handling and validation - Structured logging and performance monitoring - Security features (password sanitization, input validation) - Full test coverage and TypeScript support - Complete documentation and user guides ## 🚀 Phase 1: Privacy & Security (v1.1.0 - v1.3.0) ### 1.1 End-to-End Encryption for PII Data (v1.1.0) **Priority: HIGH** 🔴 **Goals:** - Encrypt all personally identifiable information (PII) at rest - Implement field-level encryption for sensitive data - Ensure models only see anonymized/pseudonymized data **Implementation:** - [ ] **Data Classification System** - Identify and classify PII fields (names, account numbers, transaction descriptions) - Create encryption schema for different data sensitivity levels - Implement data masking for non-encrypted fields - [ ] **Encryption Layer** - AES-256 encryption for sensitive fields - Key management system (local key storage) - Encryption/decryption utilities for database operations - Support for encrypted search and filtering - [ ] **Anonymization Engine** - Pseudonymization of account identifiers - Transaction description sanitization - Merchant name anonymization - Amount range bucketing for privacy **Technical Requirements:** - Node.js crypto module integration - Database schema updates for encrypted fields - Migration scripts for existing data - Performance impact assessment ### 1.2 Local LLM Integration via Ollama (v1.2.0) **Priority: HIGH** 🔴 **Goals:** - Enable local AI processing without external API calls - Maintain data privacy by processing data locally when using local models - Support multiple local models for different use cases **Implementation:** - [ ] **Ollama Integration** - Ollama client library integration - Model management and selection - Local model health monitoring - Fallback mechanisms for model availability - [ ] **Local Processing Pipeline** - Anonymized data preparation for local models - Context-aware prompt engineering - Response processing and formatting - Error handling for local model failures - [ ] **Model Configuration** - Configurable model selection per operation - Model-specific prompt templates - Performance optimization for local inference - Memory management for large models **Technical Requirements:** - Ollama API integration - Model configuration management - Local inference optimization - Resource monitoring and management ### 1.3 Explainable AI (XAI) Framework (v1.3.0) **Priority: HIGH** 🔴 **Goals:** - Complete transparency in AI decision-making for financial applications - Regulatory compliance with financial AI explainability requirements - Audit trails for all AI-generated insights and recommendations - User-friendly explanations for complex financial analysis **Implementation:** - [ ] **XAI Explanation Engine** - Decision tree explanations for financial recommendations - Feature importance scoring for spending analysis - Confidence intervals and uncertainty quantification - Step-by-step reasoning for complex calculations - Alternative scenario explanations - [ ] **Audit Trail System** - Complete logging of AI decision processes - Data lineage tracking for all inputs - Model versioning and change tracking - Regulatory compliance reporting - User interaction history with explanations - [ ] **Explanation Formats** - Natural language explanations for end users - Technical explanations for auditors/regulators - Visual explanations with charts and graphs - Structured explanations for API consumers - Multi-level explanations (simple to detailed) - [ ] **Compliance Features** - GDPR Article 22 compliance (automated decision-making) - Financial Services AI regulations compliance - Audit-ready documentation generation - Explanation export for regulatory submissions - Bias detection and fairness reporting ### 1.4 Advanced Privacy Controls (v1.4.0) **Priority: MEDIUM** 🟡 **Goals:** - Granular privacy controls for different data types - User-configurable anonymization levels - Audit trails for data access and processing **Implementation:** - [ ] **Privacy Configuration** - User-defined privacy levels (strict, balanced, permissive) - Field-level privacy controls - Time-based data retention policies - Geographic data handling preferences - [ ] **Audit System** - Comprehensive logging of data access - Privacy compliance reporting - Data processing audit trails - User consent tracking ## 🔧 Phase 2: Enhanced Functionality (v2.0.0 - v2.2.0) ### 2.1 Advanced Analytics & Insights (v2.0.0) **Priority: MEDIUM** 🟡 **Goals:** - AI-powered financial insights and recommendations - Predictive analytics for spending patterns - Automated financial health scoring **Implementation:** - [ ] **AI-Powered Analytics with XAI** - Spending pattern analysis with explanation of patterns found - Anomaly detection with detailed reasoning for flagged transactions - Budget optimization suggestions with step-by-step rationale - Financial goal tracking with progress explanations - Risk assessment with confidence scores and alternative scenarios - [ ] **Predictive Features with Explainability** - Cash flow forecasting with uncertainty quantification - Spending predictions with feature importance analysis - Risk assessment with decision tree explanations - Trend analysis with statistical significance reporting - Scenario modeling with "what-if" explanations ### 2.1.1 Extensive Financial Analysis Tools (v2.0.1) **Priority: HIGH** 🔴 **Goals:** - Comprehensive financial modeling and analysis capabilities - Advanced forecasting and simulation tools - Optimization algorithms for financial decisions **Implementation:** - [ ] **Financial Forecasting Engine** - Multi-variable cash flow forecasting - Seasonal trend analysis and predictions - Scenario modeling (optimistic, realistic, pessimistic) - Monte Carlo simulations for risk assessment - Time series analysis with ARIMA models - [ ] **Financial Optimization Tools** - Budget allocation optimization - Investment portfolio optimization - Debt payoff strategy optimization - Tax optimization recommendations - Expense reduction identification - [ ] **Advanced Simulation Capabilities** - "What-if" scenario modeling - Life event impact simulations (marriage, children, retirement) - Market volatility impact analysis - Economic downturn stress testing - Goal achievement probability modeling - [ ] **Financial Health Scoring** - Comprehensive financial health metrics - Credit score impact analysis - Debt-to-income ratio optimization - Emergency fund adequacy assessment - Retirement readiness scoring ### 2.1.2 Vertical-Specific Financial Tools (v2.0.2) **Priority: MEDIUM** 🟡 **Goals:** - Specialized tools for different professional segments - Industry-specific financial analysis and optimization - Tailored recommendations for different income patterns **Implementation:** - [ ] **Freelancer & Contractor Tools** - Irregular income pattern analysis - Quarterly tax estimation and planning - Business expense categorization and optimization - Client payment tracking and forecasting - Self-employment tax optimization - Retirement planning for variable income - Emergency fund sizing for irregular income - Invoice and payment cycle optimization - [ ] **Small Business Owner Tools** - Cash flow management for seasonal businesses - Inventory optimization analysis - Customer payment behavior analysis - Business loan optimization - Tax deduction maximization - Profit margin analysis and optimization - Break-even analysis and modeling - Business growth scenario planning - [ ] **Real Estate Professional Tools** - Property investment analysis - Rental income optimization - Property management expense tracking - Real estate tax optimization - Market timing analysis - Property portfolio diversification - Mortgage optimization strategies - Capital gains planning - [ ] **Healthcare Professional Tools** - Malpractice insurance optimization - Medical equipment financing analysis - Practice acquisition modeling - Student loan repayment optimization - Retirement planning for high earners - Tax optimization for medical professionals - Practice valuation and exit planning - [ ] **Technology Professional Tools** - Stock option analysis and optimization - RSU vesting schedule optimization - Startup equity evaluation - Tech industry salary benchmarking - Remote work expense optimization - Professional development investment ROI - Career transition financial planning - [ ] **Creative Professional Tools** - Project-based income optimization - Royalty and licensing income tracking - Equipment depreciation optimization - Creative project profitability analysis - Intellectual property valuation - Seasonal income smoothing strategies - Portfolio diversification for creatives ### 2.2 Unified Data Format with Provider Extensions (v2.1.0) **Priority: HIGH** 🔴 **Goals:** - Create a flexible, extensible data format that supports provider-specific features - Enable advanced analysis by leveraging unique data from different providers - Maintain backward compatibility while adding new capabilities **Implementation:** - [ ] **Unified Data Schema Design** - Core transaction schema with standard fields - Provider-specific extension fields - Metadata and enrichment data structure - Versioning and migration support - Data quality and validation framework - [ ] **Provider-Specific Extensions** - **Banking Providers**: Account routing numbers, branch codes, transaction codes - **Investment Platforms**: Security symbols, market data, portfolio allocations - **Cryptocurrency Exchanges**: Blockchain addresses, transaction hashes, gas fees - **Payment Processors**: Merchant categories, payment methods, fraud scores - **Credit Cards**: Reward points, interest rates, credit utilization - **Insurance**: Policy numbers, coverage details, claim information - **Real Estate**: Property addresses, square footage, market valuations - [ ] **Advanced Data Enrichment** - Automatic categorization using provider data - Merchant identification and verification - Geographic and temporal analysis - Risk scoring and fraud detection - Market data integration for investments - Tax categorization and optimization - [ ] **Provider Integration Framework** - Standardized API adapters for different providers - Real-time data synchronization - Batch data import/export capabilities - Data transformation and normalization - Error handling and retry mechanisms ### 2.3 Multi-Modal Data Support (v2.2.0) **Priority: MEDIUM** 🟡 **Goals:** - Support for additional data sources beyond banking - Integration with investment accounts, crypto wallets - Receipt and document processing **Implementation:** - [ ] **Extended Data Sources** - Investment account integration - Cryptocurrency wallet support - Receipt OCR and processing - Document management system - Insurance and benefits data - Real estate and property data - [ ] **Enhanced Unified Data Model** - Cross-platform data normalization - Unified transaction categorization - Multi-currency support - Asset tracking and valuation - Provider-specific data preservation - Advanced metadata management ### 2.4 Real-Time Data Processing (v2.3.0) **Priority: LOW** 🟢 **Goals:** - Real-time transaction processing - Live balance updates - Instant notifications and alerts **Implementation:** - [ ] **Real-Time Infrastructure** - WebSocket support for live updates - Event-driven architecture - Real-time data synchronization - Push notification system ## 🏗️ Phase 3: Platform & Ecosystem (v3.0.0+) ### 3.1 Plugin Architecture (v3.0.0) **Priority: MEDIUM** 🟡 **Goals:** - Extensible plugin system for custom functionality - Third-party integration capabilities - Community-driven feature development **Implementation:** - [ ] **Plugin Framework** - Plugin API and SDK - Sandboxed execution environment - Plugin marketplace and distribution - Security and validation framework ### 3.2 Multi-User Support (v3.1.0) **Priority: LOW** 🟢 **Goals:** - Support for multiple users and households - Role-based access control - Family financial management features **Implementation:** - [ ] **User Management** - Multi-user authentication - Role-based permissions - Household account management - Shared financial goals and budgets ### 3.3 Cloud & Self-Hosted Options (v3.2.0) **Priority: LOW** 🟢 **Goals:** - Flexible deployment options - Cloud hosting capabilities - Self-hosted solution with easy setup **Implementation:** - [ ] **Deployment Options** - Docker containerization - Kubernetes deployment - Cloud hosting (AWS, GCP, Azure) - One-click self-hosting setup ## 🛠️ Technical Debt & Maintenance ### Ongoing Improvements - [ ] **Performance Optimization** - Database query optimization - Caching layer implementation - Memory usage optimization - Response time improvements - [ ] **Developer Experience** - Enhanced debugging tools - Better error messages and diagnostics - Improved development workflow - Comprehensive API documentation - [ ] **Testing & Quality** - Increased test coverage - Performance testing - Security testing and audits - Load testing and scalability ## 📊 Success Metrics ### Phase 1 Metrics - **Privacy**: 100% of PII data encrypted at rest - **Performance**: <2s response time for local LLM queries - **Security**: Zero data exposure in logs or external calls - **Explainability**: 100% of AI decisions include explanations - **Compliance**: Full audit trail for all AI-generated insights - **Transparency**: All financial recommendations include confidence scores ### Phase 2 Metrics - **Functionality**: 90% user satisfaction with AI insights - **Coverage**: Support for 5+ additional data sources - **Reliability**: 99.9% uptime for real-time features - **Analysis Tools**: 95% accuracy in financial forecasting - **Vertical Tools**: 80% adoption rate among target professionals - **Optimization**: 15% average improvement in financial outcomes ### Phase 3 Metrics - **Adoption**: 1000+ active users - **Ecosystem**: 20+ community plugins - **Scalability**: Support for 10,000+ concurrent users ## 🎯 Next Steps ### Immediate Actions (Next 2 weeks) 1. **Research & Planning** - [ ] Evaluate encryption libraries and approaches - [ ] Research Ollama integration patterns - [ ] Design data classification schema - [ ] Research XAI frameworks and explanation techniques - [ ] Study financial AI compliance requirements - [ ] Create detailed technical specifications 2. **Prototype Development** - [ ] Build encryption/decryption proof of concept - [ ] Test Ollama integration with sample data - [ ] Create anonymization algorithm prototypes - [ ] Build XAI explanation engine prototype - [ ] Implement audit trail logging system - [ ] Performance benchmarking ### Short Term (Next 2 months) 1. **Phase 1.1 Implementation** - [ ] Implement data classification system - [ ] Build encryption layer - [ ] Create anonymization engine - [ ] Update database schema 2. **Testing & Validation** - [ ] Security testing and audits - [ ] Performance impact assessment - [ ] User acceptance testing - [ ] Documentation updates ## 🤝 Contributing We welcome contributions to this roadmap! Please: - Open issues for feature requests - Submit pull requests for implementations - Join discussions in the project repository - Share feedback and suggestions ## 📅 Development Phases ### Phase 1: Foundation - E2E Encryption Implementation - Local LLM Integration via Ollama - Advanced Privacy Controls - Advanced Analytics & Financial Analysis Tools ### Phase 2: Enhanced Functionality - Extensive Financial Analysis Tools - Vertical-Specific Financial Tools - Unified Data Format with Provider Extensions - Multi-Modal Data Support ### Phase 3: Platform & Ecosystem - Plugin Architecture - Multi-User Support - Cloud Deployment Options - Enterprise Features --- *This roadmap is a living document and will be updated based on community feedback, technical discoveries, and changing requirements.*