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Automated web browsing & extraction with Airtop and AI-prompted queries

Luciano GutierrezLuciano Gutierrez
943 views
2/3/2026
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This workflow contains community nodes that are only compatible with the self-hosted version of n8n.

🤖 Autonomous Web Interaction with Airtop (via MCP Trigger)

This workflow uses Airtop to perform fully automated web interactions—triggered by an AI agent through the native MCP Server Trigger in n8n.

> 💡 Perfect for browser automation, intelligent data extraction, and agent-based workflows.

✨ Features

  • ✅ Triggered via native MCP Server (no need for external LangChain services)
  • 🚀 Automates full browser sessions: open window, load page, scroll, click, fill forms
  • 🧠 Supports AI-prompt-based extraction from web content
  • 📷 Captures screenshots and waits for downloads when needed
  • 🧼 Cleans up with session and window termination
  • 🔄 Fully adaptable to agent-based automation flows

🧰 Workflow Breakdown

  1. Trigger: Native MCP Server Trigger receives instructions

  2. Create Session & Window: Starts browser automation in Airtop

  3. Load Web Page: Loads target URL

  4. Page Interaction:

    • Click elements
    • Scroll inside containers
    • Fill forms with dynamic data

  5. Extract Content:

    • Query using prompts
    • Paginated extraction

  6. Wait & Capture:

    • Waits for downloadable content
    • Takes a screenshot

  7. Cleanup:

    • Closes windows and terminates session

📦 Requirements

  • ✅ n8n 1.90+ with MCP Server Trigger
  • ✅ Active Airtop account with API credentials
  • ✅ Installed Airtop Tool node (n8n-nodes-base)
  • 🧠 External agent (optional) to send prompt/data to MCP endpoint

🔍 Use Cases

  • 🤖 Agents that browse and extract data from the web
  • 📝 Fill and submit forms from structured data
  • 🔎 Intelligent page querying using prompt-based automation
  • 🧪 Visual testing and screenshot capturing for QA workflows

🔧 Nodes Used

  • MCP Server Trigger (native)

  • Airtop Tool(native):

    • Session creation and termination
    • Window control (open, close, screenshot)
    • Interaction (click, scroll, fill)
    • Extraction (query, pagination)
    • Download waiters

🧠 AI-Automation Ready

This workflow is designed to be controlled by external AI agents or orchestration tools. Combined with prompt-based querying and DOM control, it brings a human-like browsing experience into automated pipelines.

🔗 License

Open-source under MIT. Commercial usage allowed with attribution.

Let me know if you'd like to add:

  • 🧪 A test mcp client to validate triggers
  • 🌐 A public link to the deployed workflow
  • 📎 A JSON download for users to import directly

Automated Web Browsing & Extraction with Airtop and AI-Prompted Queries

This n8n workflow serves as a foundational trigger for processes that interact with the Model Context Protocol (MCP) server. It's designed to initiate AI-driven tasks, likely involving web browsing, data extraction, or other AI-prompted queries, by acting as an entry point for MCP requests.

What it does

  1. Listens for MCP Requests: The workflow starts with an "MCP Server Trigger" node, which acts as a listener for incoming requests conforming to the Model Context Protocol. This means it's ready to receive instructions or data from an MCP client or another AI service.

Prerequisites/Requirements

  • n8n Instance: An active n8n instance where this workflow can be imported and run.
  • Model Context Protocol (MCP) Client/Service: This workflow expects to be triggered by a system or application that sends requests formatted according to the Model Context Protocol.

Setup/Usage

  1. Import the workflow: Download the provided JSON and import it into your n8n instance.
  2. Activate the workflow: Ensure the workflow is active so it can listen for incoming MCP requests.
  3. Configure MCP Client: Your MCP client or calling service should be configured to send requests to the URL exposed by this "MCP Server Trigger" node. The specific URL will be displayed in the node's settings once the workflow is activated and saved.

This workflow is a building block. To achieve specific tasks like automated web browsing or data extraction, you would extend this workflow by adding subsequent nodes that process the data received from the MCP trigger, interact with tools like Airtop, and utilize AI models based on the received prompts.

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