McFlow

# McFlow Overview ## What is McFlow? McFlow is a Model Context Protocol (MCP) server that bridges AI assistants with n8n workflow automation. It provides a structured, reliable way for AI agents to create, manage, and deploy n8n workflows without directly accessing the n8n CLI. ## Core Concepts ### Model Context Protocol (MCP) MCP is a protocol that enables AI assistants to interact with external tools and services through a standardized interface. McFlow implements this protocol to provide workflow automation capabilities. ### Node Extraction System McFlow's unique approach separates code content from workflow structure: - **Workflow JSON**: Contains node configuration and connections - **External Files**: Code, SQL, and prompts stored separately for better editing - **Automatic Injection**: Content merged back during deployment ### Project Structure Flexibility McFlow adapts to different repository layouts: - **Simple Projects**: Single `workflows/` directory - **Multi-Project Repos**: Multiple projects with individual workflow folders - **Automatic Detection**: McFlow identifies your structure automatically ## Key Benefits ### For Developers - **IDE Support**: Edit code with full syntax highlighting and IntelliSense - **Version Control**: Track changes to code separately from workflow structure - **Modularity**: Reuse code modules across workflows - **Testing**: Test code logic independently ### For AI Assistants - **Structured Interface**: Clear commands replace complex CLI operations - **Validation**: Automatic checking prevents invalid workflows - **Context Awareness**: Access to project conventions and patterns - **Error Prevention**: Guards against common mistakes ### For Teams - **Consistency**: Enforced naming conventions and structure - **Documentation**: Auto-generated workflow documentation - **Collaboration**: Clear separation of concerns - **Maintenance**: Easier to update and debug ## Use Cases ### Automation Development Create complex automation workflows with proper code management and testing. ### API Integration Build REST API endpoints with webhook triggers and HTTP responses. ### Data Processing Design ETL pipelines with database connections and transformation logic. ### Scheduled Tasks Set up recurring jobs for reports, backups, and maintenance tasks. ### Event-Driven Workflows React to external events with conditional logic and multi-path processing. ## How It Works 1. **Command Reception**: AI assistant sends McFlow commands through MCP 2. **Validation**: McFlow validates the request and parameters 3. **File Management**: Handles extraction/injection of code content 4. **n8n Integration**: Deploys validated workflows to n8n instance 5. **Feedback Loop**: Returns status and results to the AI assistant ## Design Philosophy ### Separation of Concerns Code logic separated from workflow structure for clarity and maintainability. ### Fail-Safe Operations Validation and checks prevent broken deployments and runtime errors. ### Developer Experience Optimized for both human developers and AI assistants. ### Extensibility Modular design allows for custom nodes, templates, and integrations. ## Getting Started 1. **Install McFlow**: Follow the installation guide 2. **Configure MCP Client**: Set up Claude Desktop or Continue.dev 3. **Create First Workflow**: Use templates to get started quickly 4. **Extract and Edit**: Leverage the code extraction system 5. **Deploy and Test**: Push to n8n and validate execution ## Next Steps - Review the [Architecture](architecture.md) for technical details - Check [Integration Guide](integrations.md) for client setup - See [Node Reference](nodes.md) for available n8n nodes - Read [Troubleshooting](troubleshooting.md) for common issues