Multi-Agent Architecture Knowledge MCP An MCP server that provides expert knowledge on multi-agent architectures directly to your AI coding assistants (Claude Code, Cursor, Claude Desktop). ## What does this do? When building multi-agent systems, your AI coding assistant can instantly access: - Architecture Patterns: Supervisor/Worker, Hierarchical, Swarm, Blackboard, Pipeline, Contract-Net - Framework Recommendations: LangGraph, CrewAI, AutoGen, MetaGPT, AgentScope - Implementation Strategies: Step-by-step guidance with code examples - Use Case Analysis: Pattern recommendations tailored to your requirements - Bilingual Support: English and Japanese queries How this helps: Instead of spending hours researching which architecture pattern fits your use case, your AI agent accesses curated knowledge from arXiv papers and official documentation to design better multi-agent systems in real-time. ## Setup Instructions No local installation needed - just add the URL to your MCP client configuration. ### Claude Code bash claude mcp add multi-agent-architect \ --url https://ai-crew-solutions--multi-agent-architect-mcp.apify.actor \ --token YOUR_APIFY_API_TOKEN Get your Apify API token from: https://console.apify.com/account/integrations ### Cursor 1. Open your Cursor MCP configuration file: - Global: ~/.cursor/mcp.json - Project-specific: .cursor/mcp.json in your project root 2. Add this configuration: json { "mcpServers": { "multi-agent-architect": { "url": "https://ai-crew-solutions--multi-agent-architect-mcp.apify.actor", "headers": { "Authorization": "Bearer YOUR_APIFY_API_TOKEN" } } } } 3. Restart Cursor ### Claude Desktop 1. Open your Claude Desktop configuration file: - macOS: ~/Library/Application Support/Claude/claude_desktop_config.json - Windows: %APPDATA%\Claude\claude_desktop_config.json 2. Add this configuration: json { "mcpServers": { "multi-agent-architect": { "url": "https://ai-crew-solutions--multi-agent-architect-mcp.apify.actor", "headers": { "Authorization": "Bearer YOUR_APIFY_API_TOKEN" } } } } 3. Restart Claude Desktop ## Available Tools ### search_knowledge Search the multi-agent architecture knowledge base. Parameters: - query (string, required): Your question in English or Japanese - top_k (number, optional): Number of results (default: 5) Example: Ask your AI agent: "What is the Supervisor/Worker pattern and when should I use it?" ### recommend_architecture Get architecture pattern recommendations for your specific use case. Parameters: - use_case (string, required): Description of what you're building - constraints (object, optional): Requirements like complexity, scalability Example: Ask your AI agent: "Recommend an architecture for a large-scale customer support automation system" ## Example Usage Once configured, simply ask your AI coding assistant questions like: - "I'm building a customer support chatbot with escalation. Which multi-agent pattern should I use?" - "Compare LangGraph vs CrewAI for building a research assistant with multiple specialized agents" - "What's the best architecture for parallel task processing with 10+ agents?" Your AI agent will use this MCP server to provide expert recommendations backed by research papers and production case studies. ## Knowledge Base - Architecture Patterns: 6+ major multi-agent patterns with use case mappings - Framework Comparisons: LangGraph, CrewAI, AutoGen, MetaGPT, AgentScope - Use Case Library: 20+ real-world scenarios with pattern recommendations - Sources: Curated from arXiv papers, official documentation, and production case studies ## Pricing - search_knowledge: $0.30 per query - recommend_architecture: $0.40 per recommendation Why? Saves 3-5 hours of research time per project. Pay only when your AI agent actually uses the tools. --- Version: 1.0.0 Built by: AI Crew Solutions