Analytical MCP Server

# Research Utility Completion Plan This document outlines the step-by-step plan to elevate the Research Utility completeness from 85% to 100%. Each task is numbered for reference and includes a checkbox for tracking progress. ## 🔬 Advanced NLP Enhancements - [ ] **1.1.** Enhance entity type classification with contextual analysis - [ ] **1.1.1.** Implement more sophisticated entity disambiguation - [ ] **1.1.2.** Add domain-specific entity recognition patterns - [ ] **1.1.3.** Create adaptive confidence scoring based on context - [ ] **1.2.** Refine relationship extraction quality - [ ] **1.2.1.** Add support for complex relationship patterns - [ ] **1.2.2.** Implement semantic analysis for implicit relationships - [ ] **1.2.3.** Create hierarchical relationship classification - [ ] **1.3.** Expand coreference resolution - [ ] **1.3.1.** Enhance cross-sentence reference resolution - [ ] **1.3.2.** Implement specialized handling for domain-specific references - [ ] **1.3.3.** Add confidence scoring for ambiguous references ## 🛡️ Resilience & Performance - [ ] **2.1.** Implement advanced rate limiting strategies - [ ] **2.1.1.** Add token bucket algorithm with configurable parameters - [ ] **2.1.2.** Implement adaptive rate limit based on response times - [ ] **2.1.3.** Create priority-based rate limiting for critical operations - [ ] **2.2.** Enhance error handling and recovery - [ ] **2.2.1.** Implement circuit breaker pattern with configurable thresholds - [ ] **2.2.2.** Add graceful degradation for partial API failures - [ ] **2.2.3.** Create error profiling for predictive error handling - [ ] **2.3.** Optimize caching strategies - [ ] **2.3.1.** Implement hierarchical caching with different TTL levels - [ ] **2.3.2.** Add semantic cache key generation for better hit rates - [ ] **2.3.3.** Create background refresh mechanisms for high-priority data ## 🔄 Integration & Validation - [ ] **3.1.** Enhance cross-tool integration - [ ] **3.1.1.** Implement seamless data flow between research and analytical tools - [ ] **3.1.2.** Create standard interfaces for all research outputs - [ ] **3.1.3.** Add integration points for external data sources - [ ] **3.2.** Improve research validation mechanisms - [ ] **3.2.1.** Implement multi-source cross-validation - [ ] **3.2.2.** Add confidence scoring for validation results - [ ] **3.2.3.** Create anomaly detection for inconsistent research findings - [ ] **3.3.** Enhance metadata handling - [ ] **3.3.1.** Implement comprehensive metadata tracking - [ ] **3.3.2.** Add provenance information for all research data - [ ] **3.3.3.** Create metadata-based filtering and searching ## 📊 Quality & Metrics - [ ] **4.1.** Implement advanced quality metrics - [ ] **4.1.1.** Add comprehensive research quality scoring - [ ] **4.1.2.** Implement precision and recall metrics for NLP components - [ ] **4.1.3.** Create confidence interval calculations for research results - [ ] **4.2.** Enhance monitoring and observability - [ ] **4.2.1.** Implement detailed performance tracking - [ ] **4.2.2.** Add usage pattern analysis - [ ] **4.2.3.** Create predictive resource utilization models - [ ] **4.3.** Add automated testing capabilities - [ ] **4.3.1.** Implement test data generators for NLP components - [ ] **4.3.2.** Create benchmark suites for performance testing - [ ] **4.3.3.** Add regression testing for all enhancements ## 📚 Documentation & Examples - [ ] **5.1.** Create comprehensive API documentation - [ ] **5.1.1.** Document all research utility endpoints with examples - [ ] **5.1.2.** Add integration guides for each component - [ ] **5.1.3.** Create troubleshooting documentation with common issues - [ ] **5.2.** Develop advanced usage examples - [ ] **5.2.1.** Create end-to-end research workflow examples - [ ] **5.2.2.** Add domain-specific research examples - [ ] **5.2.3.** Develop cross-tool integration examples - [ ] **5.3.** Enhance developer guides - [ ] **5.3.1.** Create architecture documentation with diagrams - [ ] **5.3.2.** Add performance optimization guides - [ ] **5.3.3.** Develop contribution guidelines for extensions ## Implementation Strategy ### Phase 1: Core Enhancements (Weeks 1-2) Focus on enhancing the core NLP capabilities and resilience mechanisms: - Tasks 1.1, 1.2, 2.1, 2.2 ### Phase 2: Integration & Quality (Weeks 3-4) Improve integration points and implement quality metrics: - Tasks 3.1, 3.2, 4.1, 4.2 ### Phase 3: Documentation & Refinement (Weeks 5-6) Complete documentation and refine all components: - Tasks 5.1, 5.2, 5.3 ### Phase 4: Final Testing & Optimization (Weeks 7-8) Conduct comprehensive testing and final optimizations: - Tasks 1.3, 2.3, 3.3, 4.3 ## Success Criteria The Research Utility will be considered 100% complete when: 1. All NLP components achieve >90% accuracy on benchmark datasets 2. API resilience mechanisms handle >99% of failure scenarios gracefully 3. Integration with all analytical tools is seamless and well-documented 4. Comprehensive test coverage exceeds 90% for all components 5. All documentation is complete and up-to-date 6. Performance metrics meet or exceed target thresholds ## Monitoring & Reporting Progress will be tracked weekly with: 1. Completion percentage for each main task area 2. Performance metrics for NLP components 3. Test coverage statistics 4. Documentation completeness assessment ## Conclusion This plan provides a structured approach to achieving 100% completeness for the Research Utility. By methodically addressing each area, we will ensure comprehensive capabilities, robust performance, and excellent documentation for all research-related functionality.