Analytical MCP Server

# Analytical MCP Server Development Plan ## 🚀 Phase 1: Project Setup & Configuration ### Environment Setup - [x] Install Node.js (v20+) and npm - [x] Create project directory structure - [x] Initialize Git repository - [x] Configure TypeScript (tsconfig.json) - [x] Set up linting and formatting tools - [x] Initialize npm project with `package.json` ### Dependencies - [x] Install core dependencies: - [x] `@modelcontextprotocol/sdk` - [x] `zod` (for schema validation) - [x] `dotenv` (for environment variables) - [x] `papaparse` (for data processing) - [x] `mathjs` (for mathematical operations) - [x] NLP libraries (`natural`, `sentiment`, etc.) - [x] Install development dependencies: - [x] TypeScript - [x] ESLint - [x] Prettier - [x] Jest (for testing) ## 🔧 Phase 2: Core Functionality Implementation ### Research Integration - [x] Develop Exa Research Utility (`exa_research.ts`) - [x] Configurable web and news search - [x] Basic fact extraction mechanism - [x] Data validation support - [x] Create Research Integration Tool (`research_integration.ts`) - [x] Cross-domain research generation - [x] Confidence scoring for insights - [x] Data enrichment capabilities - [x] Implement comprehensive error handling - [x] Add advanced fact extraction techniques (Exa-based) ### Analytical Tool Development - [x] Implement dataset analysis tool - [x] Create decision analysis capabilities - [x] Develop regression analysis tool - [x] Build hypothesis testing utility - [x] Implement logical argument analysis and fallacy detection ## 🧠 Phase 3: Integration and Enhancement ### Tool Integration - [x] Integrate research utility with analytical tools - [x] Develop comprehensive error handling - [x] Implement tool wrapper functionality - [x] Create modular tool registration system - [x] Build caching system for research results ### Advanced Features - [x] Implement performance-optimized caching mechanism - [x] Create NLP toolkit with advanced text processing (Exa-based) - [x] Develop logical fallacy detection system - [x] Build perspective shifting capabilities - [x] Implement data visualization generator - [x] Consolidate NLP capabilities on Exa API (removing Hugging Face dependency) ## 🧪 Phase 4: Testing and Documentation ### Testing Framework - [x] Set up Jest testing infrastructure - [x] Create test utilities and helpers - [x] Implement unit tests for core tools - [x] Configure test environment with API validation - [x] Create tests for Exa-based NLP implementation - [ ] Expand integration test coverage ### Documentation - [x] Document architecture and design principles - [x] Create API testing guide - [x] Document security practices and system requirements - [x] Develop comprehensive examples - [x] Add advanced NLP documentation - [x] Document Exa-based NLP implementation - [x] Create consolidated environment variable requirements ## 🚧 Challenges and Mitigation - API Rate Limits: - [x] Implemented sophisticated caching strategy with TTL management - [x] Added configurable staleness thresholds - [x] Consolidated to single API provider (Exa) for NLP - [ ] Need to implement more robust backoff strategies - Research Reliability: - [x] Added multi-source verification - [x] Implemented confidence scoring - [x] Enhanced NLP capabilities with Exa-based entity recognition - [ ] Improve validation across diverse domains - Performance: - [x] Optimized research query strategies - [x] Implemented memory-efficient algorithms - [x] Created fallback mechanisms for offline operation - [ ] Need to profile and improve query execution times ## 🔜 Next Immediate Steps 1. Refine advanced NLP implementation: - Optimize entity type classification - Enhance relationship extraction quality - Improve confidence scoring algorithms 2. Implement robust API rate limit handling with backoff strategies 3. Enhance integration test coverage 4. Create comprehensive real-world examples 5. Profile and optimize performance of NLP operations ## 📊 Progress Metrics - Research Utility Completeness: 85% - Tool Integration: 95% - Error Handling: 100% - Testing: 75% - Documentation: 90% - API Consolidation: 100% ## 🎯 Upcoming Focus Areas ### Short-Term (1-3 months) 1. Refine Exa-based NLP capabilities 2. Address code duplication in utility functions 3. Enhance monitoring and logging for API interactions 4. Implement circuit breakers for external API dependencies 5. Update all dependencies and address security vulnerabilities ### Medium-Term (3-6 months) 1. Expand entity recognition and relationship extraction quality 2. Develop more sophisticated caching strategies for high-volume scenarios 3. Add advanced analytical tools for specialized domains 4. Improve documentation with inline comments 5. Implement cross-validation between different analytical tools ### Long-Term (6-12 months) 1. Implement multi-modal analysis capabilities 2. Develop explainable AI components for research validation 3. Create an API-first design for external integration 4. Build collaborative analysis features 5. Enhance visualization generation capabilities