The core technological innovation of N8N2MCP is its MCP routing system optimized for AI scenarios. The system adopts Flask+Playwright technology stack with the following key features:
- Connection pooling management: reuse n8n session connections to reduce authentication time consumption by 80%
- Parallel processing: support for 100+ concurrent workflow execution requests
- Smart caching: memory-level caching of workflow templates for HF calls
Benchmarking of the router shows that the end-to-end latency from the time the AI assistant initiates a request to the time it gets the workflow result is controlled within 300ms. Configured through the .env file of theMCP_PORTcap (a poem)FLASK_PORTparameters, users can flexibly adjust the service port. In debugging mode, developers can use the--log-level debugparameter to get the complete request processing log for easy performance optimization.
The system also integrates Supabase for configuration persistence, ensuring that workflow state is not lost after a service restart.
This answer comes from the articleN8N2MCP: automated tool to convert n8n workflows to MCP serversThe
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