home / mcp / enhanced architecture mcp server
The architecture main mcp servers
Configuration
View docs{
"mcpServers": {
"autoexecbatman-arch-mcp": {
"command": "node",
"args": [
"D:\\arch_mcp\\enhanced_architecture_server_context.js"
]
}
}
}You are deploying Enhanced Architecture MCP servers that provide professional accuracy, tool safety, user preference handling, and intelligent context monitoring. These MCP servers coordinate to deliver structured reasoning, safe tooling, and efficient context management across multiple components, improving reliability and governance for AI-assisted workflows.
To use this MCP setup with your MCP client, you run the central node-based servers and connect your client to the configured MCP endpoints. The enhanced architecture server handles accuracy and safety checks, the chain-of-thought server manages reasoning strands, and the local AI server delegates heavy tasks to a local model when available. Your client will interact with these servers through the MCP coordination layer, which orchestrates workflows like creating reasoning strands, delegating analysis, storing insights, and updating architectural patterns.
Prerequisites: install Node.js and npm on your system.
1. Install dependencies for the MCP setup.
2. Prepare the MCP configuration that defines how each server runs and where it is located on your machine.
3. Run the MCP servers using the runtime commands shown below.
4. Optionally enable the local AI integration by installing Ollama and pulling a model as needed.
{
"mcpServers": {
"enhanced_arch": {
"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": {
"command": "node",
"args": ["D:\\arch_mcp\\local-ai-server.js"],
"env": {}
}
}
}
````
````json
{
"mcpServers": {
"enhanced_arch": {
"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": {
"command": "node",
"args": ["D:\\arch_mcp\\local-ai-server.js"],
"env": {}
}
}
}
````
- Local AI setup (optional): install Ollama and pull models
````bash
ollama pull llama3.1:8b
````
- Start commands come from the runtime configuration shown above, using the Node environment to run each server script.Security and safety are built into the MCP layers. You will see professional accuracy checks, tool safety enforcement, and a mechanism to store and apply user preferences across sessions. The system tracks context tokens across documents, artifacts, tool calls, and system overhead, and it provides 80% and 90% capacity warnings to help you manage long-running conversations.
Server Connection Issues: verify Node.js version compatibility, ensure file paths in configuration are correct, and review server logs for syntax errors.
Context Tracking: monitor token estimation accuracy, adjust conversation length limits, and use reset tools for fresh sessions.
Performance: local AI requires Ollama installation; context monitoring adds small overhead; pattern storage is optimized for fast responses.
The MCP setup focuses on multi-MCP orchestration, reasoning strand management, memory-backed pattern storage, and user preference evolution. It emphasizes empirical validation with multiple gates and token-efficient context handling to maintain high-quality, grounded outputs.