n8n Workflow Builder MCP Server

Build n8n workflows from text using AI - Built by FlowEngine

Turn natural language into production-ready, validated n8n workflows. This isn't just another n8n context provider, it's a complete workflow generation engine with built-in validation, auto-fixing, and architectural intelligence.

this MCP is built to provide validated n8n workflows — not just context.

- 13-Point Validation Engine - Catches errors before you import
- Auto-Fix Malformed Workflows — Automatically repairs common issues
- Architecture Recommendations — Suggests optimal workflow patterns
- Security Scanning — Detects credential leaks and vulnerabilities
- Performance Analysis — Identifies bottlenecks and optimization opportunities
- Real Parameter Schemas — Loaded directly from n8n packages for accuracy
- 600+ Registered Node Types — Only real nodes, no hallucinations

Result: Workflows that actually import and run on first try.

Quick Start

Local Install | Remote Server (no install)

Video Demo


Feature Details

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Every workflow passes through 13 validation checks before output:

1. Node Type Validation — Verifies every node type exists in n8n
2. Connection Integrity — Ensures all connections reference existing nodes
3. Parameter Type Checking — Validates parameter types match node schemas
4. Required Fields — Checks all required parameters are present
5. Credential References — Validates credential configurations
6. Expression Syntax — Checks n8n expression syntax (={{ }})
7. Position Validation — Ensures nodes have valid canvas positions
8. Duplicate Detection — Catches duplicate node names
9. Orphan Node Detection — Finds disconnected nodes
10. Trigger Validation — Ensures workflows have proper entry points
11. Loop Detection — Identifies potential infinite loops
12. Output Mapping — Validates data flow between nodes
13. Version Compatibility — Checks node version compatibility

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When validation finds issues, the engine automatically repairs them:

- Missing positions → Auto-calculates layout on canvas
- Invalid node names → Generates unique, valid names
- Missing connections array → Initializes proper structure
- Incorrect typeVersion → Updates to current supported version
- Malformed parameters → Applies sensible defaults
- Broken JSON structure → Attempts recovery and repair

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Based on your task description, the engine analyzes keywords and intent to suggest the optimal pattern. Here's when each is recommended:

#### Regular Workflows (Deterministic)

Linear Pipeline
- When: Simple, predictable data transformations
- Example: "Get data from API → Transform → Save to database"
- Best for: ETL, data sync, scheduled reports
- Why not AI: No decision-making needed, faster execution, lower cost

Conditional Branching
- When: Known decision points with clear rules
- Example: "If order > $100, send to manager; else auto-approve"
- Best for: Approval flows, routing, rule-based automation
- Why not AI: Rules are explicit, no reasoning required

Parallel Processing
- When: Independent operations that can run simultaneously
- Example: "Send email AND update CRM AND log to Slack"
- Best for: Notifications, multi-system updates, batch processing
- Why not AI: No dependencies between branches

Event-Driven
- When: Reacting to external triggers
- Example: "When webhook received → process → respond"
- Best for: API endpoints, real-time integrations, chatbots
- Why not AI: Response is formulaic, not conversational

#### AI Agent Workflows (Autonomous)

When to use AI Agents instead of regular workflows:

| Use AI Agent When... | Use Regular Workflow When... |
|---------------------|------------------------------|
| Task requires reasoning | Steps are predictable |
| Input is unstructured (natural language) | Input is structured (JSON, forms) |
| Multiple tools might be needed dynamically | Tool sequence is known |
| Conversation/context matters | Stateless processing |
| Decision logic is complex or fuzzy | Rules are explicit |

Single AI Agent
- When: One autonomous entity with access to tools
- Example: "Customer support bot that can search docs, create tickets, and escalate"
- Architecture: Chat Trigger → AI Agent (with tools) → Response
- Tools: Calculator, Code, HTTP Request, custom tools
- Memory: Optional conversation memory for context

AI Agent with Memory
- When: Conversation context matters across messages
- Example: "Personal assistant that remembers user preferences"
- Architecture: Chat Trigger → Memory Load → AI Agent → Memory Save → Response
- Memory types: Buffer (recent), Window (last N), Summary (compressed)

Multi-Agent Workflows

For complex tasks, multiple specialized agents can collaborate:

Sequential Multi-Agent
- When: Task has distinct phases requiring different expertise
- Example: "Research Agent → Analysis Agent → Writing Agent"
- Flow: Agent 1 output becomes Agent 2 input
- Best for: Content pipelines, multi-step reasoning

Supervisor Pattern
- When: Need coordination between specialist agents
- Example: "Supervisor routes to: Sales Agent, Support Agent, or Technical Agent"
- Flow: Supervisor Agent decides which specialist handles the task
- Best for: Customer service, complex routing

Parallel Agents
- When: Multiple perspectives needed simultaneously
- Example: "Analyst Agent AND Risk Agent AND Compliance Agent all review"
- Flow: Split → Multiple Agents → Merge results
- Best for: Review processes, multi-criteria evaluation

Hierarchical Agents
- When: Complex orchestration with sub-tasks
- Example: "Manager Agent delegates to Team Agents who use Tool Agents"
- Flow: Top-level agent breaks down task, delegates, aggregates
- Best for: Large-scale automation, enterprise workflows

#### How the Engine Decides

The recommendation engine analyzes your description for:

1. AI keywords: "chat", "conversation", "understand", "decide", "reason" → suggests AI Agent
2. Automation keywords: "sync", "transform", "schedule", "trigger" → suggests Regular Workflow
3. Complexity signals: Multiple conditions, dynamic routing → suggests Conditional or Agent
4. Tool mentions: "search", "calculate", "call API" → adds appropriate tools to Agent
5. Memory signals: "remember", "context", "history" → adds memory to Agent

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Detects vulnerabilities before deployment:

- Hardcoded credentials — API keys, passwords, tokens in plain text
- Insecure protocols — HTTP instead of HTTPS
- Missing authentication — API calls without auth headers
- Sensitive data exposure — PII in logs or outputs
- Code injection risks — Unsafe code node patterns

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Identifies bottlenecks and optimization opportunities:

- Execution time estimates — Per-node and total workflow
- Parallel path detection — Opportunities for concurrent execution
- API call optimization — Batch vs. individual requests
- Memory usage patterns — Large data handling concerns
- Rate limiting risks — High-frequency API calls

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Parameters are loaded directly from n8n packages, not guessed:

- Accurate defaults — Real default values from node definitions
- Correct types — String, number, boolean, options validated
- Required vs optional — Knows which fields are mandatory
- Nested structures — Complex parameter objects handled correctly

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No hallucinations. The AI can only use nodes that actually exist:

- Every node type is loaded from the official n8n-nodes-base package
- LangChain nodes from @n8n/n8n-nodes-langchain included
- Node registry is generated directly from n8n source code
- If a node doesn't exist in the registry, it won't be used
- Prevents the common AI problem of inventing fake node types

---

Quick Start

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Choose the method that works best for you:

#### 1. Smithery (One-Click Install)

Easy local installation for Claude Desktop. View on Smithery

``bash
npx @smithery/cli install @Ami3466/mcp-flowengine-n8n-workflow-builder --client claude
`

Note: Smithery has a 50 uses/day limit. For unlimited access, use the remote server below.

#### 2. Remote Server (More Stable - Unlimited) ⭐

Connect to our hosted server - no installation required!

Add this to your Claude Desktop config:

Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json

`json
{
"mcpServers": {
"flowengine-n8n": {
"url": "https://mcp-flowengine-n8n-workflow-builder.onrender.com/mcp",
"transport": "http"
}
}
}
`

Benefits:
- ✅ More stable - Dedicated hosting infrastructure
- ✅ Unlimited usage - No daily limits
- ✅ Always up-to-date - Automatically updated
- ✅ Zero installation - Just add config and restart Claude
- ✅ Free - Community hosted

#### 3. npm (Manual Install)

For all other MCP clients:

`bash
npm install -g flowengine-n8n-workflow-builder
`

---

Setup by Platform

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#### Option A: Smithery (Local Install)

View on Smithery

`bash
npx @smithery/cli install @Ami3466/mcp-flowengine-n8n-workflow-builder --client claude
`

Restart Claude Desktop after installation.

Note: Limited to 50 uses/day. For unlimited access, use the remote server below.

#### Option B: Remote Server (More Stable - Unlimited) ⭐

No installation required! Just add config and restart.

1. Edit Claude Desktop config:
- Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
- Windows: %APPDATA%/Claude/claude_desktop_config.json

2. Add this configuration:
`json
{
"mcpServers": {
"flowengine-n8n": {
"url": "https://mcp-flowengine-n8n-workflow-builder.onrender.com/mcp",
"transport": "http"
}
}
}
`

3. Restart Claude Desktop (fully quit and reopen)

4. Verify connection:
- Look for the 🔌 MCP icon in Claude Desktop
- You should see "flowengine-n8n" listed with green status

Benefits: More stable, unlimited usage, no local installation, always updated.

#### Option C: Manual Local Install

1. Install the package:
`bash
npm install -g flowengine-n8n-workflow-builder
`

2. Edit Claude Desktop config:
- Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
- Windows: %APPDATA%/Claude/claude_desktop_config.json

3. Add this configuration:
`json
{
"mcpServers": {
"flowengine-n8n": {
"command": "flowengine-n8n"
}
}
}
`

4. Restart Claude Desktop (fully quit and reopen)

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1. Install the package globally:
`bash
npm install -g flowengine-n8n-workflow-builder
`

2. Open VS Code Settings (Cmd/Ctrl + ,)

3. Search for "MCP"

4. Add MCP Server:
- Click "Edit in settings.json"
- Add to claude.mcpServers:
`json
{
"claude.mcpServers": {
"flowengine-n8n": {
"command": "flowengine-n8n"
}
}
}
`

5. Reload VS Code (Cmd/Ctrl + Shift + P → "Developer: Reload Window")

6. Start using:
- Open Claude Code panel
- Ask Claude to build n8n workflows
- The MCP server will be automatically available

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1. Install the package:
`bash
npm install -g flowengine-n8n-workflow-builder
`

2. Open Cursor Settings:
- Mac: Cursor → Settings → Features
- Windows/Linux: File → Preferences → Features

3. Navigate to MCP Settings:
- Scroll to "Model Context Protocol"
- Click "Edit Config"

4. Add configuration:
`json
{
"mcpServers": {
"flowengine-n8n": {
"command": "flowengine-n8n"
}
}
}
`

5. Restart Cursor

6. Verify:
- Open Cursor's AI chat
- The MCP server should be available
- Try: "Create an n8n workflow for me"

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1. Install the package:
`bash
npm install -g flowengine-n8n-workflow-builder
`

2. Open Cline Settings in VS Code:
- Open Command Palette (Cmd/Ctrl + Shift + P)
- Type "Cline: Open Settings"

3. Add MCP Server:
- In Cline settings, find "MCP Servers"
- Add new server:
`json
{
"flowengine-n8n": {
"command": "flowengine-n8n"
}
}
`

4. Reload VS Code

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1. Install the package:
`bash
npm install -g flowengine-n8n-workflow-builder
`

2. Open Continue config:
- Open Command Palette (Cmd/Ctrl + Shift + P)
- Type "Continue: Open config.json"

3. Add MCP server to config:
`json
{
"mcpServers": {
"flowengine-n8n": {
"command": "flowengine-n8n"
}
}
}
`

4. Reload Continue extension

---

What This Does

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Describe what you want in plain language:
- "Monitor my email and notify me on Slack"
- "Build an AI chatbot with memory and tools"
- "Sync data between Google Sheets and my database"
- "Create a customer support automation workflow"

Your AI assistant generates complete, working n8n workflows.

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Your AI gets access to:
- 600+ Node Types - All n8n-nodes-base and LangChain nodes
- Real Parameter Schemas - Loaded directly from n8n packages
- Best Practices - Workflow design patterns and optimizations
- Intelligent Validation - Automatic error detection and fixing

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Workflow Generation:
- Build workflows from natural language descriptions
- Add, edit, and delete nodes programmatically
- Connect nodes and manage workflow structure
- Get detailed workflow analysis

Intelligence & Suggestions:
- Architecture recommendations (linear, conditional, AI agent, etc.)
- Node suggestions for specific tasks
- Workflow analysis and improvements
- Natural language explanations

Quality & Security:
- Comprehensive workflow validation
- Security vulnerability scanning
- Performance analysis and bottleneck detection
- Dry-run workflow testing

Templates & Search:
- Pre-built workflow templates
- Search 600+ nodes by keyword
- Browse nodes by category
- Get detailed node documentation

---

Usage Examples

Once installed, ask your AI assistant to help with workflows:

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`
"Create an n8n workflow that monitors Gmail for emails with 'urgent' in the subject and sends a Slack notification to #alerts"
`

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`
"Analyze this workflow and suggest improvements"
[paste your workflow JSON]
`

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`
"What nodes should I use to build a customer onboarding automation?"
`

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`
"Validate this workflow and fix any errors"
[paste workflow JSON]
`

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`
"Scan this workflow for security issues"
[paste workflow JSON]
`

---

Available Tools

Your AI assistant gets access to 23 powerful tools:

Workflow Building:
- build_workflow - Generate workflows from descriptions
- add_node - Add nodes to existing workflows
- edit_node - Modify node parameters
- delete_node - Remove nodes
- add_connection - Connect nodes
- get_workflow_details - Analyze workflow structure

Validation & Quality:
- validate_workflow - Comprehensive validation with auto-fix
- test_workflow - Dry-run simulation
- scan_security - Security vulnerability detection
- analyze_performance - Performance and bottleneck analysis

Intelligence:
- suggest_architecture - Recommend workflow patterns
- suggest_nodes - Node recommendations for tasks
- analyze_workflow - Deep workflow insights
- suggest_improvements - Optimization suggestions
- explain_workflow - Natural language explanations

Node Library:
- search_nodes - Search 600+ nodes
- list_nodes_by_category - Browse by category
- get_node_details - Detailed node documentation

Templates:
- list_templates - Browse workflow templates
- get_template - Get specific templates
- search_templates - Search templates

Utilities:
- extract_workflow_json - Extract JSON from text
- fix_json - Repair malformed JSON

---

Deploy Your Workflows

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Build for free, deploy instantly:

1. Generate workflow using this MCP server
2. Visit flowengine.cloud
3. Import your workflow JSON
4. Test and deploy - no infrastructure needed

Why FlowEngine.cloud?
- ✅ No server setup or management
- ✅ Built-in monitoring and logs
- ✅ Automatic scaling
- ✅ Visual workflow editor
- ✅ Free tier available

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1. Generate workflow using this MCP server
2. Open your n8n instance
3. Import JSON (... → Import from File)
4. Configure credentials and activate

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Troubleshooting

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1. Verify installation:
`bash
which flowengine-n8n
# Should show: /usr/local/bin/flowengine-n8n (or similar)
`

2. Test the server manually:
`bash
flowengine-n8n
# Should start the MCP server
`

3. Check Claude Desktop logs:
- Mac: ~/Library/Logs/Claude/mcp*.log
- Windows: %APPDATA%/Claude/logs/mcp*.log

4. Restart your AI client completely (fully quit and reopen)

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`bash


Update npm

npm install -g npm@latest

Reinstall the package

npm uninstall -g flowengine-n8n-workflow-builder
npm install -g flowengine-n8n-workflow-builder
`

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Mac/Linux:
`bash
sudo npm install -g flowengine-n8n-workflow-builder
`

Windows:
Run PowerShell/CMD as Administrator

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`bash


Clear npm cache

npm cache clean --force

Try manual installation instead

npm install -g flowengine-n8n-workflow-builder
`

---

How It Works

This MCP server connects your AI assistant to expert n8n knowledge:

`
You describe what you want
↓
Your AI Tool (Claude/Cursor/etc.)
↓
MCP Protocol
↓
FlowEngine n8n Builder
↓
Expert n8n Knowledge + Validation
↓
Complete, Working Workflow
``

What Your AI Gets:
- Deep n8n expertise
- 600+ node definitions with real schemas
- Workflow patterns and best practices
- Validation and auto-fixing capabilities
- Security and performance analysis

What You Get:
- Production-ready workflows
- Properly validated JSON
- Working configurations
- Best practice implementations

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Features

✅ Build workflows from text - Natural language to working n8n workflows
✅ 600+ Node Types - Full n8n-nodes-base and LangChain support
✅ Real Parameter Schemas - Loaded directly from n8n packages
✅ Intelligent validation - Automatic error detection and fixing
✅ Security scanning - Detect vulnerabilities and sensitive data
✅ Performance analysis - Find bottlenecks and optimize
✅ Works with any LLM - Universal MCP protocol support
✅ No API keys needed - Works completely offline
✅ Built by FlowEngine - Production-tested technology
✅ Deploy anywhere - FlowEngine.cloud or self-hosted

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About FlowEngine

FlowEngine is a platform for building and deploying n8n workflows with AI:

- 🎨 Visual Builder - Drag-and-drop editor at flowengine.cloud
- 🤖 AI-Powered - Generate workflows with natural language
- ☁️ Managed Hosting - Deploy instantly, no DevOps needed
- 📈 Production Ready - Monitoring, logs, and scaling included
- 🆓 Free Tier - Try and build workflows for free

This MCP server brings FlowEngine's workflow generation technology to your local development environment.

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Use Cases

For Developers:
- Generate boilerplate workflows quickly
- Prototype automation ideas fast
- Learn n8n patterns and best practices
- Build complex workflows with AI assistance

For Teams:
- Accelerate workflow development
- Standardize workflow patterns
- Reduce learning curve for n8n
- Scale automation initiatives

For Businesses:
- Automate repetitive tasks
- Connect systems and tools
- Build custom integrations
- Deploy AI-powered workflows

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Support & Resources

- FlowEngine Platform: flowengine.cloud
- Documentation: docs.flowengine.cloud
- npm Package: View on npm
- Smithery Registry: Install via Smithery
- GitHub Repository: View Source
- Report Issues: Contact FlowEngine support

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License

MIT License with Commons Clause

✅ You CAN:
- Use for personal projects (free forever)
- View and study the source code
- Modify for your own personal use
- Contribute improvements back

❌ You CANNOT:
- Sell this software
- Offer as a commercial/paid service
- Build competing products using this code
- Use in any commercial product or service

For commercial licensing: flowengine.cloud

Full license: LICENSE

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Built by FlowEngine - Enterprise-grade n8n workflow automation platform

Website • Documentation • npm • Smithery