n8n AI Agent Integration

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Overview

• Autonomous research agents in workflows
• Lead enrichment and CRM automation
• Competitor monitoring workflows
• Multi-platform data aggregation pipelines
• Scheduled intelligence gathering with n8n cron
• AI-powered workflow automation

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Download Pre-Built Workflow

• Pre-configured AI Agent with MCP connection
• Example prompts and use cases
• Error handling and logging
• Ready-to-customize structure

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Prerequisites

• Self-hosted n8n instance or n8n.cloud account
• AnySite account with MCP Unlimited subscription
• Basic understanding of n8n workflows and AI Agent node

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Step 1: Get Your MCP Connection URL

1

Log in to AnySite

Visit app.anysite.io and sign in to your account

2

Navigate to MCP Integration

Go to the MCP Server Integration section in your dashboard

3

Copy Direct URL

You’ll see two connection options:

• OAuth URL: https://mcp.anysite.io/mcp
• Direct URL: https://mcp.anysite.io/mcp?api_key=YOUR_KEY

Click Copy next to the Direct URL - this includes your API key for authentication.

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Step 2: Configure MCP in n8n AI Agent

1

Create or Open Workflow

Open n8n and create a new workflow or open an existing one where you want to use the AI Agent.

2

Add AI Agent Node

1. Click the + button to add a new node
2. Search for “AI Agent” or “@n8n/n8n-nodes-langchain.agent”
3. Add the AI Agent node to your canvas

3

Configure MCP Connection

In the AI Agent node configuration:

1. Go to the Tools section
2. Click Add Tool
3. Select MCP Tool from the list
4. In the MCP Tool configuration:
   • Server URL: Paste your Direct URL from Step 1
     Copy

     https://mcp.anysite.io/mcp?api_key=YOUR_API_KEY
     
   • Name: “AnySite MCP” (or any descriptive name)
5. Click Save or OK

4

Verify Connection

After saving, the MCP tools should appear in your AI Agent’s available tools list. You can verify by:

• Looking at the Tools section - should show “68 tools available”
• Testing with a simple prompt in n8n’s test mode

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Step 3: Configure Agent Behavior

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System Prompt Example

You are an AI research agent integrated into an n8n workflow with access to AnySite MCP tools.
Use these tools to extract data from LinkedIn, Instagram, Twitter, Reddit, YouTube, SEC, and Y Combinator.

When the user requests information:
1. Identify the appropriate MCP tool(s) to use
2. Extract the data using the tools
3. Analyze and structure the information
4. Return JSON-formatted results for the workflow

Available platforms: LinkedIn, Instagram, Twitter, Reddit, YouTube, SEC EDGAR, Y Combinator, Web Parser

Always provide accurate, well-structured data that can be processed by subsequent workflow nodes.

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Available Tools

• Profile extraction and search
• Company research and employee data
• Post and comment analysis
• LinkedIn messaging and chat
• Job listings and groups

• Profile data and metrics
• Posts and reels extraction
• Comments and likes analysis
• Follower/following data

• Profile information
• Tweet extraction and search
• User discovery

• Post details and search
• Comments extraction
• User posts and comments

• Video search and metadata
• Video details and engagement
• Subtitle/transcript extraction

• Company profiles by slug
• Search YC companies
• Search YC founders

• Search SEC EDGAR filings
• Retrieve SEC documents

• Webpage parsing
• Sitemap extraction
• Web search

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Use Cases & Examples

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1. Lead Enrichment Workflow

Webhook → AI Agent (MCP) → Enrich Contact → Save to CRM

"Research {{$json.email}} on LinkedIn. Find their profile, current company,
job title, and recent activity. Return structured JSON."

1. Uses find_linkedin_user_email to find profile
2. Uses get_linkedin_profile to extract details
3. Uses get_linkedin_user_posts for activity
4. Returns structured JSON to workflow

• Enriched contact data flows to next node
• Automatic CRM update
• Email personalization data ready

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2. Competitor Monitoring Workflow

Schedule (Daily) → AI Agent (MCP) → Format Report → Send to Slack

"Monitor competitor 'TechCorp' on LinkedIn:
- Get last 7 days of posts
- Extract engagement metrics
- Identify trending topics
Return JSON with summary."

• search_linkedin_companies to find company
• get_linkedin_company_posts for recent posts
• Agent analyzes and structures findings

• Runs daily at 9 AM
• Automatic Slack notification
• No manual intervention needed

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3. Multi-Platform Research Pipeline

Manual Trigger → AI Agent (MCP) → Split Data → Parallel Processing → Merge Results → Output

"Find all mentions of 'AI automation tools' across:
- LinkedIn (posts from last week)
- Twitter (tweets from last week)
- Reddit (discussions from last month)
- YouTube (videos from last 3 months)

Analyze sentiment and provide summary."

• search_linkedin_posts
• search_twitter_posts
• search_reddit_posts
• search_youtube_videos
• Agent synthesizes multi-platform insights

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4. SEC Financial Intelligence Workflow

HTTP Request → AI Agent (MCP) → Extract Financial Data → Store in Database

"Get latest 10-K and 10-Q filings for {{$json.company_name}}.
Extract key financial metrics, risk factors, and MD&A summary."

• search_sec_companies to find filings
• get_sec_document to retrieve full document
• Agent parses and extracts structured data

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5. Y Combinator Startup Research

Airtable Trigger → AI Agent (MCP) → YC Research → Update Airtable

"Research YC company {{$json.slug}}:
- Get company profile
- Find founders and backgrounds
- Identify batch and status
Return structured data for database."

• get_yc_company for company profile
• search_yc_founders for team data
• Structured output for Airtable update

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Integration with n8n Workflows

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Basic Workflow Structure

1. Trigger - Webhook, Schedule, or Manual trigger
2. AI Agent - Processes request using MCP tools
3. Process Results - Code or Set node to structure data
4. Output - Send to CRM, Database, Slack, or Email

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Multi-Agent Setup

• Research Agent - Gathers data using MCP tools
• Analysis Agent - Processes and analyzes the data
• Merge & Report - Combines insights and generates output

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Error Handling

• Use IF node after AI Agent to check success
• Create separate paths for success and error cases
• Log errors to database or send notifications
• Implement retry logic for failed operations

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n8n-Specific Features

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1. Expression Integration

// Get data from previous AI Agent node
{{ $json.research_results }}

// Access specific fields
{{ $json.linkedin_profile.name }}
{{ $json.company_data.employee_count }}

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2. Loop Through Results

1. Split In Batches - Divide input data into manageable chunks
2. AI Agent - Process each item with MCP tools
3. Aggregate Results - Combine all processed data

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3. Conditional Tool Usage

// In AI Agent system prompt
"If the request is about LinkedIn, use linkedin_* tools.
If it's about SEC filings, use search_sec_companies and get_sec_document.
If it's about YouTube, use youtube_* tools.
Always choose the most appropriate tool for the data source."

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4. JSON Output for Workflow

"Return your findings as JSON with this structure:
{
  'summary': 'Brief overview',
  'data': { extracted data },
  'sources': ['tool1', 'tool2'],
  'timestamp': 'ISO format'
}
"

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Workflow Templates

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Lead Enrichment Pipeline

1. Webhook - Receives email/name/company
2. AI Agent (MCP) - Enriches with LinkedIn data
3. Code Node - Structures and validates data
4. HubSpot/Salesforce - Updates CRM
5. Slack - Notifies team

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Daily Competitor Report

"Monitor these 3 competitors on LinkedIn:
- Company A
- Company B
- Company C

For each, get:
- Last 5 posts
- Engagement metrics
- Employee growth trends

Return JSON report."

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Social Media Intelligence

"Research {{$json.topic}} across all platforms:
- LinkedIn posts (last week)
- Twitter mentions (last week)
- Reddit discussions (last month)
- YouTube videos (last 3 months)

Analyze sentiment and trends."

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Troubleshooting

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Security Best Practices

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Benefits of n8n + MCP Integration

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Advanced Integration Patterns

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1. Multi-Agent Research Pipeline

Trigger
  → Agent 1: LinkedIn Research (MCP)
  → Agent 2: Twitter Sentiment (MCP)
  → Agent 3: Synthesis & Analysis
  → Send Report

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2. Conditional Data Enrichment

Input Data
  → IF node (has LinkedIn URL?)
      → Yes: AI Agent extracts LinkedIn data
      → No: AI Agent searches by email/name
  → Merge results
  → Continue workflow

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3. Feedback Loop with Memory

Get Task
  → AI Agent (MCP): Initial Research
  → Store in n8n Database
  → IF: Need more data?
      → Yes: AI Agent (MCP): Deep Dive
      → Loop back
  → Final Output

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4. Real-Time Monitoring

Schedule (Every hour)
  → AI Agent: Check for new posts/filings
  → IF: Found new content?
      → Process and alert
      → Update tracking database
  → Sleep until next run

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n8n Workflow Best Practices

"Search LinkedIn for 'data engineers' in Berlin.
Return JSON array with exactly these fields:
- name, title, company, profile_url
Limit to 20 results."

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Next Steps

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Support