Enhanced Architecture MCP

# Enhanced Architecture MCP Enhanced Model Context Protocol (MCP) servers with professional accuracy, tool safety, user preferences, and intelligent context monitoring. ## Overview This repository contains a collection of MCP servers that provide advanced architecture capabilities for AI assistants, including: - **Professional Accuracy Enforcement** - Prevents marketing language and ensures factual descriptions - **Tool Safety Protocols** - Blocks prohibited operations and validates parameters - **User Preference Management** - Stores and applies communication and aesthetic preferences - **Intelligent Context Monitoring** - Automatic token estimation and threshold warnings - **Multi-MCP Orchestration** - Coordinated workflows across multiple servers ## Active Servers ### Enhanced Architecture Server (`enhanced_architecture_server_context.js`) Primary server with complete feature set: - Professional accuracy verification - Tool safety enforcement - User preference storage/retrieval - Context token tracking - Pattern storage and learning - Violation logging and metrics ### Chain of Thought Server (`cot_server.js`) Reasoning strand management: - Create and manage reasoning threads - Branch reasoning paths - Complete strands with conclusions - Cross-reference reasoning history ### Local AI Server (`local-ai-server.js`) Local model integration via Ollama: - Delegate heavy reasoning tasks - Token-efficient processing - Hybrid local+cloud analysis - Model capability queries ## Installation 1. **Prerequisites:** ```bash npm install ``` 2. **Configuration:** Update your Claude Desktop configuration to include the servers: ```json { "mcpServers": { "enhanced-architecture": { "command": "node", "args": ["D:\\arch_mcp\\enhanced_architecture_server_context.js"], "env": {} }, "cot-server": { "command": "node", "args": ["D:\\arch_mcp\\cot_server.js"], "env": {} }, "local-ai-server": { "command": "node", "args": ["D:\\arch_mcp\\local-ai-server.js"], "env": {} } } } ``` 3. **Local AI Setup (Optional):** Install Ollama and pull models: ```bash ollama pull llama3.1:8b ``` ## Usage ### Professional Accuracy Automatically prevents: - Marketing language ("revolutionary", "cutting-edge") - Competitor references - Technical specification enhancement - Promotional tone ### Context Monitoring Tracks conversation tokens across: - Document attachments - Artifacts and code - Tool calls and responses - System overhead Provides warnings at 80% and 90% capacity limits. ### User Preferences Stores preferences for: - Communication style (brief professional) - Aesthetic approach (minimal) - Message format requirements - Tool usage patterns ### Multi-MCP Workflows Coordinates complex tasks: 1. Create CoT reasoning strand 2. Delegate analysis to local AI 3. Store insights in memory 4. Update architecture patterns ## Key Features - **Version-Free Operation** - No version dependencies, capability-based reporting - **Empirical Validation** - 60+ validation gates for decision-making - **Token Efficiency** - Intelligent context management and compression - **Professional Standards** - Enterprise-grade accuracy and compliance - **Cross-Session Learning** - Persistent pattern storage and preference evolution ## File Structure ``` D:\arch_mcp\ ├── enhanced_architecture_server_context.js # Main server ├── cot_server.js # Reasoning management ├── local-ai-server.js # Local AI integration ├── data/ # Runtime data (gitignored) ├── backup/ # Legacy server versions └── package.json # Node.js dependencies ``` ## Development ### Architecture Principles - **Dual-System Enforcement** - MCP tools + text document protocols - **Empirical Grounding** - Measurable validation over assumptions - **User-Centric Design** - Preference-driven behavior adaptation - **Professional Standards** - Enterprise accuracy and safety requirements ### Adding New Features 1. Update server tool definitions 2. Implement handler functions 3. Add empirical validation gates 4. Update user preference options 5. Test cross-MCP coordination ## Troubleshooting **Server Connection Issues:** - Check Node.js version compatibility - Verify file paths in configuration - Review server logs for syntax errors **Context Tracking:** - Monitor token estimation accuracy - Adjust limits for conversation length - Use reset tools for fresh sessions **Performance:** - Local AI requires Ollama installation - Context monitoring adds ~50ms overhead - Pattern storage optimized for < 2ms response ## License MIT License - see individual files for specific licensing terms. ## Contributing Architecture improvements welcome. Focus areas: - Enhanced token estimation accuracy - Additional validation gates - Cross-domain pattern recognition - Performance optimization