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:

   npm install
   

2. Configuration: Update your Claude Desktop configuration to include the servers:

   {
     "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:

   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