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

Related MCP server: MCP Toolkit

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