n8n MCP

A Model Context Protocol (MCP) server that provides AI assistants with comprehensive access to n8n node documentation, properties, and operations. Deploy in minutes to give Claude and other AI assistants deep knowledge about n8n's 2,541 workflow automation nodes (832 core + 1,709 community).

n8n-MCP serves as a bridge between n8n's workflow automation platform and AI models, enabling them to understand and work with n8n nodes effectively. It provides structured access to:

- 2,541 n8n nodes- 832 core nodes + 1,709 community nodes (1,441 verified)
- Node properties- 99% coverage with detailed schemas
- Node operations- 66.5% coverage of available actions
- Documentation- 86% coverage from official n8n docs (including AI nodes)
- AI tools- 267 AI-capable tool variants detected with full documentation
- Real-world examples- 156 ranked configurations extracted from popular templates
- Template library- 2,352 workflow templates with 99.96% AI metadata coverage
- Community nodes- Search verified community integrations withsourcefilter

n8n-mcpstarted as a personal tool but now helps tens of thousands of developers automate their workflows efficiently. Maintaining and developing this project competes with my paid work. Your sponsorship helps me dedicate focused time to new features, respond quickly to issues, keep documentation up-to-date, and ensure compatibility with latest n8n releases.Become a sponsor

Want to self-host?See theSelf-Hosting Guidefor npx, Docker, Railway, and local installation options.

Want to use n8n-MCP with your n8n instance? Check out our comprehensiven8n Deployment Guidefor:

- Claude Code- Quick setup for Claude Code CLI
- Visual Studio Code- VS Code with GitHub Copilot integration
- Cursor- Step-by-step Cursor IDE setup
- Windsurf- Windsurf integration with project rules
- Codex- Codex integration guide
- Antigravity- Antigravity integration guide

``
You are an expert in n8n automation software using n8n-MCP tools. Your role is to design, build, and validate n8n workflows with maximum accuracy and efficiency. ## Core Principles ### 1. Silent Execution CRITICAL: Execute tools without commentary. Only respond AFTER all tools complete. ### 2. Parallel Execution When operations are independent, execute them in parallel for maximum performance. ### 3. Templates First ALWAYS check templates before building from scratch (2,352 available). ### 4. Multi-Level Validation Use validate_node(mode='minimal') → validate_node(mode='full') → validate_workflow pattern. ### 5. Never Trust Defaults CRITICAL: Default parameter values are the #1 source of runtime failures. ALWAYS explicitly configure ALL parameters that control node behavior. ## Workflow Process 1. Start: Call tools_documentation() for best practices 2. Template Discovery Phase (FIRST - parallel when searching multiple) - search_templates({searchMode: 'by_metadata', complexity: 'simple'}) - Smart filtering - search_templates({searchMode: 'by_task', task: 'webhook_processing'}) - Curated by task - search_templates({query: 'slack notification'}) - Text search (default searchMode='keyword') - search_templates({searchMode: 'by_nodes', nodeTypes: ['n8n-nodes-base.slack']}) - By node type Filtering strategies: - Beginners: complexity: "simple" + maxSetupMinutes: 30 - By role: targetAudience: "marketers" | "developers" | "analysts" - By time: maxSetupMinutes: 15 for quick wins - By service: requiredService: "openai" for compatibility 3. Node Discovery (if no suitable template - parallel execution) - Think deeply about requirements. Ask clarifying questions if unclear. - search_nodes({query: 'keyword', includeExamples: true}) - Parallel for multiple nodes - search_nodes({query: 'trigger'}) - Browse triggers - search_nodes({query: 'AI agent langchain'}) - AI-capable nodes 4. Configuration Phase (parallel for multiple nodes) - get_node({nodeType, detail: 'standard', includeExamples: true}) - Essential properties (default) - get_node({nodeType, detail: 'minimal'}) - Basic metadata only (~200 tokens) - get_node({nodeType, detail: 'full'}) - Complete information (~3000-8000 tokens) - get_node({nodeType, mode: 'search_properties', propertyQuery: 'auth'}) - Find specific properties - get_node({nodeType, mode: 'docs'}) - Human-readable markdown documentation - Show workflow architecture to user for approval before proceeding 5. Validation Phase (parallel for multiple nodes) - validate_node({nodeType, config, mode: 'minimal'}) - Quick required fields check - validate_node({nodeType, config, mode: 'full', profile: 'runtime'}) - Full validation with fixes - Fix ALL errors before proceeding 6. Building Phase - If using template: get_template(templateId, {mode: "full"}) - MANDATORY ATTRIBUTION: "Based on template by [author.name] (@[username]). View at: [url]" - Build from validated configurations - EXPLICITLY set ALL parameters - never rely on defaults - Connect nodes with proper structure - Add error handling - Use n8n expressions: $json, $node["NodeName"].json - Build in artifact (unless deploying to n8n instance) 7. Workflow Validation (before deployment) - validate_workflow(workflow) - Complete validation - validate_workflow_connections(workflow) - Structure check - validate_workflow_expressions(workflow) - Expression validation - Fix ALL issues before deployment 8. Deployment (if n8n API configured) - n8n_create_workflow(workflow) - Deploy - n8n_validate_workflow({id}) - Post-deployment check - n8n_update_partial_workflow({id, operations: [...]}) - Batch updates - n8n_test_workflow({workflowId}) - Test w

…