n8n Video Compilation MCP Server
Enables AI agents to control and optimize n8n workflows, including video compilation tasks, workflow execution monitoring, batch processing, and smart query optimization for automated content creation.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@n8n Video Compilation MCP Servercreate a compilation of trending tech tutorials from trusted channels"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
๐ฅ Insurance Claims Processing Automation Demo
Transform manual claims processing from 3 hours to 3 minutes with AI-powered automation
This n8n workflow demonstrates how insurance companies can automate their claims processing with AI, reducing processing time by 90% while improving fraud detection and customer communication.
๐ฏ Demo Impact
For a company processing 500 claims/month:
Time savings: 1,500 hours/month โ 125 hours/month
Cost savings: $75,000/month โ $6,750/month
Annual savings: ~$819,000
ROI: 13,600% (after automation costs)
Related MCP server: n8n Manager for AI Agents
๐ Quick Start (5 Minutes)
Prerequisites
Docker installed on your machine
OpenAI API key (get from platform.openai.com)
Email account for notifications
1. Start the Demo
# Clone or download this repository
# Navigate to the project directory
./start-demo.sh2. Access n8n
Open: http://localhost:5678
Username:
adminPassword:
insurance_demo_2024
3. Import the Workflow
Click "Add Workflow"
Menu (โฏ) โ "Import from File"
Upload
insurance-claims-workflow.json
4. Configure Credentials
OpenAI: Settings โ Credentials โ Add โ OpenAI API
Email: Settings โ Credentials โ Add โ Email (SMTP)
5. Activate & Test
Click "Active" toggle
Run:
./test-claims.shWatch the magic happen! โจ
๐ Project Files
n8n-money/
โโโ ๐ insurance-claims-workflow.json # Main n8n workflow
โโโ ๐ demo-presentation.md # Sales presentation slides
โโโ ๐งช sample-claim-data.json # Test scenarios
โโโ โ๏ธ docker-compose.yml # Easy Docker setup
โโโ ๐ start-demo.sh # One-click startup
โโโ ๐งช test-claims.sh # Automated testing
โโโ ๐ setup-guide.md # Detailed setup guide
โโโ ๐ local-setup-guide.md # Local installation guide
โโโ ๐ README.md # This file๐ Workflow Overview
What It Does
Receives Claims via webhook (web form, email, API)
AI Processing extracts key information from claim data
Fraud Detection scores claims 1-10 for suspicious patterns
Smart Routing sends high-risk claims to senior adjusters
Customer Communication sends instant status updates
Task Management creates appropriate staff assignments
Workflow Paths
Auto Claims (< $10K): Standard 3-5 day processing
High-Value Claims (> $10K): Senior adjuster review within 24h
Fraud Suspected: Immediate investigation priority
Property Claims: Specialist assignment with contractor verification
๐ฌ Demo Script
Opening Hook (30 seconds)
"I'm going to show you how we can reduce your claims processing from 3 hours to 3 minutes. Watch this..."
Live Demo (2 minutes)
Submit a claim via webhook
Watch AI processing in real-time
Show results: Customer emails, staff tasks, fraud scores
ROI Discussion (3 minutes)
Calculate their specific savings
Show fraud detection benefits
Discuss scalability without hiring
๐ Test Scenarios
Run ./test-claims.sh to see:
Standard Claim ($3,200) โ Approved automatically
High-Value Claim ($15,750) โ Senior review required
Suspicious Claim โ Fraud investigation triggered
Property Claim โ Specialist assignment
๐ ๏ธ Customization Options
Industry Adaptations
Auto Insurance: VIN lookup, police reports, repair networks
Property Insurance: Weather data, contractor estimates, permits
Health Insurance: Provider networks, medical codes, pre-auth
Life Insurance: Beneficiary verification, documentation
Integrations
CRM: Salesforce, HubSpot, Pipedrive
Email: Gmail, Outlook, SendGrid
Storage: Google Drive, Box, SharePoint
Tasks: Asana, Monday.com, Jira
Communication: Slack, Teams, WhatsApp
๐ฐ Business Case
Current Manual Process (Per Claim)
Data entry: 45 minutes
Policy lookup: 30 minutes
Risk assessment: 60 minutes
Communication: 30 minutes
Task creation: 15 minutes
Total: 3 hours
With Automation (Per Claim)
Automatic processing: 2 minutes
Human review (when needed): 15 minutes
Total: 2-17 minutes
Benefits Beyond Time
Fraud Detection: AI catches 85% more fraud than manual review
Customer Satisfaction: Instant confirmations vs days of waiting
Compliance: Complete audit trails and documentation
Scalability: Handle 10x volume without hiring
Accuracy: 99%+ vs 90-95% manual data entry
๐ง Troubleshooting
Common Issues
# n8n won't start
docker logs n8n-insurance-demo
# Webhook not working
curl -X POST http://localhost:5678/webhook/new-claim
# Restart everything
docker-compose down && docker-compose up -dGetting Help
๐ฏ Next Steps
For Your Demo
Customize emails with your company branding
Add your specific fraud detection rules
Connect to your existing systems
Practice the demo flow until smooth
For Production
Cloud Hosting (AWS, DigitalOcean, n8n Cloud)
Database Setup for persistence and analytics
Security Configuration with proper auth and SSL
Team Training on workflow management
๐ Demo Objection Handling
"What about complex claims?" โ 80% handled automatically, 20% routed to experts who can focus on what matters
"What if AI makes mistakes?"
โ Human oversight for high-value claims, 99%+ accuracy vs 90-95% manual
"Integration concerns?" โ Connects to your existing systems, no replacement needed
"Cost concerns?" โ $500/month saves $68,000/month = 13,600% ROI
๐ Success Metrics
After implementing, measure:
Time reduction: Hours per claim
Cost savings: Monthly processing costs
Fraud detection: Cases caught vs missed
Customer satisfaction: Response time improvements
Staff productivity: Focus on complex work
๐ฌ NEW: AI-Powered Video Compilation System
MCP Integration for AI Agents
This project now includes an advanced Model Context Protocol (MCP) server that enables AI agents like Claude, GPT, and other LLMs to directly control and optimize video compilation workflows.
๐ Quick Start - Video Compilation
# Install MCP dependencies
npm install
# Start the MCP server
npm run start:mcp
# Test the integration
npm run test:mcp๐ค AI Agent Capabilities
With MCP integration, AI agents can:
๐ฏ Smart Query Optimization: "lebron dunk" โ "LeBron James dunks compilation highlights"
๐ Quality Analysis: Real-time AI scoring of video content (1-10 scale)
๐ Channel Trust System: Filter by 16+ trusted sports channels (NBA, ESPN, etc.)
๐ฆ Batch Processing: Create multiple compilations simultaneously
๐ Error Recovery: Intelligent retry with parameter adjustments
๐ Performance Insights: Detailed analytics and recommendations
๐ ๏ธ Available MCP Tools
compile_video- Create AI-enhanced video compilationsoptimize_search_query- Smart query enhancementbatch_compile- Multiple compilations at oncelist_workflows- View available n8n workflowsget_workflow_status- Monitor workflow execution
๐ฌ Example AI Interactions
User: "Create a compilation of LeBron James dunking highlights"
AI Agent:
Optimizes query โ "LeBron James dunks compilation highlights NBA"
Executes enhanced workflow with quality filtering
Analyzes results and provides insights
Suggests improvements if needed
User: "Make compilations for the top 5 NBA players' signature moves"
AI Agent:
Batch processes: LeBron dunks, Curry threes, Giannis blocks, etc.
Monitors all executions simultaneously
Provides comparative analysis of results
Recommends optimal parameters for future compilations
๐ Video Compilation Files
n8n-money/
โโโ ๐ฌ video-compilation-workflow-enhanced.json # AI-enhanced workflow
โโโ ๐ค mcp-server.js # MCP server for AI agents
โโโ โ๏ธ mcp-config.json # MCP configuration
โโโ ๐ mcp-integration-guide.md # Detailed MCP setup guide
โโโ ๐งช test-mcp-server.js # MCP functionality tests
โโโ ๐ video-compilation-enhanced-setup.md # Enhanced workflow setup
โโโ ๐งช test-enhanced-compilation.sh # Workflow testing script๐ Integration with Claude Desktop
Add to your Claude Desktop config:
{
"mcpServers": {
"n8n-video-compilation": {
"command": "node",
"args": ["/path/to/n8n-money/mcp-server.js"],
"env": {
"N8N_BASE_URL": "http://localhost:5678/api",
"N8N_API_KEY": "your_api_key"
}
}
}
}๐ Performance Benefits
โก Speed: AI-optimized queries find better content faster
๐ฏ Quality: Smart filtering ensures high-quality compilations
๐ Automation: Full workflow automation with AI oversight
๐ Learning: AI improves strategies based on results
๐ ๏ธ Efficiency: Batch processing saves time and resources
See mcp-integration-guide.md for complete setup instructions and advanced usage examples.
๐ฅ๏ธ NEW: Interactive Workflow Manager CLI
Professional Workflow Management Made Easy
Introducing a user-friendly command-line interface for managing n8n workflows with enterprise-grade features:
๐ Quick Start - Workflow Manager
# Using bun (recommended)
bun run workflow-manager
# Or use the short alias
bun run wm
# Traditional node
npm run workflow-managerโจ Key Features
๐ Smart Discovery: Automatically scans and catalogs all workflows
โ Advanced Validation: Comprehensive syntax and configuration checking
๐ Credential Management: Detects and guides credential setup
๐ค Smart Import: Multi-endpoint API support with intelligent retry
๐ ๏ธ Integration Tools: Access to all existing debug and validation tools
๐จ Beautiful UI: Color-coded, intuitive interface for technical and non-technical users
๐ Main Menu Options
๐ Discover & View Workflows - Scan and display all available workflows
โ
Validate Workflow - Check workflow syntax and configuration
๐ Manage Credentials - Scan and manage credential requirements
๐ค Import Workflow to n8n - Deploy workflows to your n8n instance
๐ง Test n8n Connection - Verify API connectivity
๐ ๏ธ Run Integration Tools - Access existing debug and validation tools
โ Help & Usage - Show detailed help information
๐ช Exit - Exit the CLI๐ฏ Perfect for Both Technical & Non-Technical Users
For Developers:
JSON validation with detailed error reporting
Node connection analysis and optimization suggestions
API endpoint testing across multiple n8n versions
Integration with existing debug tools
For Business Users:
Clean, intuitive menu navigation
Clear status indicators and progress feedback
Plain-language error messages and solutions
Guided credential setup process
๐ Workflow Discovery Example
๐ Available Workflows:
1. โ ๐ ๐ Stock Analysis Assistant
โโ AI-powered stock analysis using RSI and MACD indicators
โโ 25 nodes, 20.1KB, modified 12/15/2024
2. โ ๐ Video Generation Workflow
โโ Enhanced video compilation with AI analysis
โโ 15 nodes, 29.3KB, modified 12/14/2024
3. โ Insurance Claims Workflow
โโ Invalid JSON - Parse Error
โโ 18 nodes, 11.2KB, modified 12/10/2024
Legend: โ=Valid โ=Invalid ๐=Webhook ๐=Credentials๐ง Environment Setup
# Required for import/deployment
export N8N_BASE_URL="http://localhost:5678"
export N8N_API_KEY="your_n8n_api_key_here"
# Optional for advanced workflows
export YOUTUBE_API_KEY="your_youtube_api_key_here"๐ก๏ธ Enterprise-Grade Features
Error Recovery: Graceful handling of all failure scenarios
Multi-API Support: Works with different n8n versions and configurations
Security Validation: Detects hardcoded credentials and security issues
Comprehensive Logging: Detailed operation logs for troubleshooting
Integration Ready: Seamlessly works with existing project tools
๐ Detailed Documentation
See docs/workflow-manager-guide.md for:
Complete feature documentation
Step-by-step tutorials
Troubleshooting guide
Advanced usage examples
Integration instructions
๐ Ready to Transform Your Workflows?
Whether you're automating insurance claims or creating AI-powered video compilations, this project demonstrates how n8n workflows combined with AI agents can revolutionize business processes.
Your insurance friends will be amazed at how quickly and accurately this processes claims that would take their team hours of manual work!
Start Your Demo
./start-demo.shNeed help? Check the detailed guides in setup-guide.md and local-setup-guide.md
Available Tools
5 toolsbatch_compileD
| Name | Required | Description | Default |
|---|---|---|---|
| queries | Yes | Array of search queries to compile | |
| settings | No | Global settings for all compilations |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Tool has no description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has no description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has no description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Tool has no description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compile_videoD
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query for video compilation (e.g., "LeBron dunking") | |
| max_clips | No | Maximum number of clips (default: 5) | |
| quality_threshold | No | AI quality score threshold 1-10 (default: 7) | |
| duration_preference | No | Preferred clip duration (default: medium) | medium |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Tool has no description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has no description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has no description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Tool has no description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_workflow_statusD
| Name | Required | Description | Default |
|---|---|---|---|
| workflowId | Yes | ID of the workflow to check |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Tool has no description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has no description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has no description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Tool has no description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_workflowsD
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Tool has no description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has no description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has no description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Tool has no description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
optimize_search_queryD
| Name | Required | Description | Default |
|---|---|---|---|
| original_query | Yes | Original search query to optimize | |
| previous_results | No | Previous compilation results for learning |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Tool has no description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has no description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has no description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Tool has no description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
Tool names suggest distinct purposes: batch_compile for batch compilation, compile_video for single, get_workflow_status and list_workflows for workflow management, optimize_search_query for search optimization. No obvious overlap, but without descriptions minor ambiguity between batch_compile and compile_video remains.
All tools use snake_case, but verb placement varies: 'batch_compile' uses an adjective verb pattern, while 'compile_video' uses a verb_noun pattern. Other tools follow standard verb_noun (list_workflows, get_workflow_status, optimize_search_query). The inconsistency in the verb position lowers the score.
With 5 tools, the server is slightly sparse but still reasonable for a video compilation domain. Each tool likely serves a core function, though some expected tools (e.g., upload, delete) are missing, making the scope feel limited.
The tool set covers compilation (single and batch) and basic workflow monitoring (status, list), but lacks workflow creation, update, or deletion. The inclusion of 'optimize_search_query' seems tangential to the main purpose, indicating possible gaps in the core video compilation workflow.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
YouTube transcripts, search, channels, playlists and bulk transcript jobs for AI agents. 14 tools.
YouTube transcripts, search, channel/playlist listings and upload tracking for AI agents. No signup.
Turn long videos into short, captioned viral clips from your AI assistant. 28 tools, OAuth.
Build and run visual creative-production workflows from your AI agent.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to manage n8n automation workflows through natural language commands, including creating, executing, monitoring, and organizing workflows with full CRUD operations and execution management.1112MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI agents to manage n8n workflow automation instances through tools for workflow CRUD operations, execution monitoring, and webhook triggering. It facilitates programmatic interaction with n8n instances via the n8n API with AI-optimized descriptions and error handling.62MIT
- AlicenseBqualityDmaintenanceEnables AI agents to search for YouTube videos and play them in the default browser as a playlist via natural language commands. It leverages the YouTube Data API v3 to provide specific tools for video discovery and media playback orchestration.2271MIT
- AlicenseBqualityBmaintenanceEnables AI agents to search, analyze, and extract insights from YouTube videos including transcripts, visual frames, and benchmarks without requiring API keys. Supports semantic search across playlists, sentiment analysis, and visual content indexing with automatic fallback chains for reliable access.4165734MIT
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/hew/n8n-money'
If you have feedback or need assistance with the MCP directory API, please join our Discord server