Intelligent Architecture Recommendation Engine is an automated infrastructure planning tool that generates tailored system architecture recommendations based on business parameters. The tool analyzes key metrics like QPS throughput, concurrent user load, database requirements, and AI model specifications to produce complete deployment blueprints. Key features: - Parameter-driven architecture generation using inputs like QPS, concurrent users, daily active users, business type (web/AI), database type (relational/NoSQL/analytics), and AI model size (small/medium/large) - Automated resource allocation recommendations covering CPU, memory, network configuration, and GPU inference clusters - Middleware selection guidance including Redis cache sizing, eviction strategies, and message queue configurations - Architecture pattern recommendations such as microservices vs. monolith and distributed system design - Deployment strategy suggestions with cloud provider recommendations - Exportable deliverables including Markdown reports and Mermaid architecture diagrams

mcp-system-infra

🚀 Intelligent Architecture Recommendation Engine: Tailored for Your System

In today's rapidly evolving digital landscape, how can you quickly and efficiently build a scalable and reliable technical infrastructure? The Intelligent Architecture Recommendation Engine is here to solve that challenge.

Based on key parameters—QPS (queries per second), concurrent users, daily active users, business type, database choice, and AI model size—this tool automatically generates:

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✨ Key Benefits

✅ Fully Parameter-Driven, Business-Oriented

Simply provide the following parameters:

The system will automatically assess and recommend:

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🗺️ Architecture Recommendation Diagram

The system automatically generates a Mermaid diagram to clearly represent component relationships:

flowchart TD
  User[User Request] --> Nginx[Nginx Load Balancer]
  Nginx --> Service[Main Business Service Node]
  Service --> DB[Database]
  Service --> Redis[Redis Cache]
  Service --> MQ[Message Queue]
  Service --> GPU[AI Inference GPU Node]
  MQ --> Consumer[Asynchronous Consumer]

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▶️ Quick Start

CLI

MCP Server Configuration

~~~json { "mcpServers": { "mcp-system-infra": { "command": "npx", "args": [ "-y", "mcp-system-infra" ] } } } ~~~

MCP Example:

Please help design a web-based system architecture report with the following specifications:

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💭 Murmurs

This project is for educational and internal use only. Contributions and feedback are welcome. For feature customization, web deployment, or enterprise integration, please contact the project maintainer.

Contact

Business Contact Email: deeppathai@outlook.com

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🧠 MCP Access Addresses