Product Introduction

1. N8N2MCP is an open-source automation platform that converts N8N workflows into Model Context Protocol (MCP) servers through a visual interface. It enables seamless integration of automation workflows with AI assistants like Claude, Cursor, and Super Chain without requiring coding expertise. The system automatically parses N8N JSON configurations to generate API endpoints compliant with MCP specifications for AI tool interoperability.

2. The core value lies in bridging no-code workflow automation with AI-powered execution environments. It eliminates the need for manual API development by transforming N8N's node-based logic into production-ready MCP servers. This allows non-technical users to deploy enterprise-grade automation tools that AI systems can directly utilize through standardized protocols.

Main Features

1. The platform features dual architecture with separate components for workflow management (Agent Marketplace) and server execution (MCP Router). The Flask-based Agent Marketplace provides web UI for browsing 50+ pre-built templates, analyzing workflow dependencies, and managing credentials through Supabase integration. The FastAPI-powered MCP Router handles dynamic endpoint creation with load balancing for high-concurrency AI requests.

2. Automatic credential mapping enables secure integration with 300+ third-party services through N8N's ecosystem. The system extracts authentication requirements from workflow nodes using Playwright-powered analysis and stores credentials using AES-256 encryption with Argon2 key derivation. Users configure API keys through an interactive form that validates permissions before deployment.

3. Real-time workflow debugging includes built-in testing tools for payload simulation and execution monitoring. The platform generates OpenAPI 3.0 documentation for each MCP server and provides WebSocket endpoints for streaming AI responses. Deployment metrics track CPU/memory usage through integrated Prometheus monitoring with Grafana dashboards.

Problems Solved

1. The product addresses the technical barrier between visual workflow automation and AI-assisted execution environments. Traditional methods require manual conversion of N8N workflows into API endpoints using custom code, which introduces maintenance overhead and security risks. This solution automates the translation process while maintaining compatibility with N8N's native execution engine.

2. Primary users include automation engineers seeking AI integration and product teams implementing no-code solutions. The platform specifically serves organizations using N8N for business process automation that require Claude/Cursor integration. Technical buyers range from DevOps engineers managing MCP deployments to AI developers building assistant toolchains.

3. Typical use cases involve creating AI-executable workflows for data processing, multi-service API orchestration, and real-time notification systems. Examples include transforming CSV data through AI-enhanced validation nodes, automating Jira ticket creation via natural language commands, and deploying Slack moderation bots with Claude content analysis capabilities.

Unique Advantages

1. Unlike competing workflow-to-API converters, N8N2MCP preserves full N8N runtime compatibility while adding MCP protocol support. The solution uniquely combines credential auto-discovery with N8N instance integration, whereas alternatives like Pipedream require complete workflow reimplementation. This maintains access to N8N's 400+ native nodes and version control features.

2. The dual architecture design separates workflow configuration from runtime execution, enabling zero-downtime updates and A/B testing of MCP endpoints. Innovative features include automatic OpenAPI schema generation from N8N nodes and browser-based credential validation using Playwright automation. The system supports hybrid deployments with both cloud-hosted and self-managed N8N instances.

3. Competitive advantages include native integration with Supabase for real-time configuration sync and enterprise-grade security through Row Level Security (RLS). The open-source MIT license allows commercial use without vendor lock-in, contrasting with proprietary alternatives. Performance benchmarks show 3x faster workflow deployment compared to manual API development methods.

Frequently Asked Questions (FAQ)

1. What prerequisites are needed for deployment? The system requires Python 3.11+, a running N8N instance (cloud or self-hosted), and Supabase project credentials. Playwright must be installed for browser-based N8N authentication, with Chromium binaries deployed through Docker in production environments. Network access between MCP Router and N8N instance is mandatory.

2. How are credentials secured during workflow execution? Credentials are stored in Supabase using Argon2id hashing with separate encryption keys per tenant. During runtime, credentials are injected into N8N through temporary environment variables that auto-expire after 15 minutes. The system never writes credentials to disk and supports HashiCorp Vault integration for enterprise users.

3. Which AI platforms are officially supported? The MCP implementation works with Claude, Cursor, and Super Chain through their native MCP clients. The protocol supports both REST and WebSocket communication, compatible with any platform implementing MCP v1.2+ specifications. Custom adapters can be developed for ChatGPT Plugins through the provided SDK.

4. Can I debug workflows after MCP conversion? Yes, the platform provides full request/response logging through the MCP Router's admin interface. Users can replay historical executions with original payload data and view real-time node execution graphs. Error tracking integrates with Sentry and includes automated rollback for failed deployments.

5. Is self-hosting supported for enterprise environments? The system supports Docker-based deployment with Kubernetes manifests provided in the repository. Enterprise features include LDAP/SSO authentication, audit logging, and horizontal scaling of MCP Router instances. A Helm chart enables deployment on AWS ECS or Google Cloud Run with auto-scaling configurations.