This article provides a complete, practical walkthrough of the Code Manual Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   Automating LinkedIn Data Scraping and Analysis Using n8n, Bright Data MCP, and Google Gemini
   
   Meta Description:
   Explore how to build a powerful, no-code workflow for scraping LinkedIn data, analyzing company info, and automating content generation using n8n, Bright Data’s MCP tools, and Google Gemini AI. Ideal for lead generation, research, and content marketing automation.
   
   Keywords:
   LinkedIn scraping, n8n automation, Bright Data MCP, Google Gemini AI, LinkedIn company data extractor, LinkedIn person profile scraper, LinkedIn automation, content generation from web data, no-code data workflows, AI-powered content extraction
   
   Third-party APIs and Services Used:
   1. Bright Data MCP Server (Bright Data MCP Client)
   2. Google Gemini (via PaLM API)
   3. Webhook.site URL (for testing HTTP responses)
   4. n8n (open-source workflow automation tool)
   
   —
   
   Article:
   
   Automating LinkedIn Data Scraping and Analysis Using n8n, Bright Data MCP, and Google Gemini
   
   In the era of intelligent automation and generative AI, businesses and researchers need tools that go beyond collecting raw data — they must transform that information into meaningful insights and content. This article introduces a robust no-code solution using n8n, Bright Data’s Managed Code Package (MCP) API, and Google Gemini to scrape, analyze, and generate natural language content from LinkedIn profiles and company pages.
   
   From web scraping and data transformation to AI-generated summaries and automated file storage, this end-to-end workflow enables seamless integration and powerful automation to help marketers, researchers, and developers streamline their LinkedIn data processes.
   
   Why This Workflow Matters
   
   LinkedIn contains a wealth of structured professional information that can power lead generation, market analysis, and competitive intelligence. However, manually extracting and interpreting this data can be time-consuming and error-prone. Combining Bright Data's web scraping tools, n8n's automation engine, and advanced AI like Google Gemini enables users to scale their data workflows effectively — entirely without writing code.
   
   Let’s break down what this n8n workflow does and the technologies involved.
   
   Step 1: User-Triggered Automation
   
   The journey starts with a manual trigger node in n8n, labeled “When clicking ‘Test workflow’.” This allows users to run the workflow on demand — ideal for testing or executing the process on specific targets like individual LinkedIn profiles or company pages.
   
   Step 2: Define Input URLs
   
   The workflow uses two set nodes:
   - One for a LinkedIn person profile (e.g., https://www.linkedin.com/in/ranjan-dailata/)
   - Another for a LinkedIn company page (e.g., https://www.linkedin.com/company/bright-data/)
   
   Each node also defines a return webhook URL (using webhook.site in this case) to catch and inspect the data responses from the scraper modules.
   
   Step 3: Scraping with Bright Data MCP
   
   Bright Data’s MCP Client API is used twice in this workflow — once for scraping person profiles and once for company pages. The tools retrieved from Bright Data are:
   - web_data_linkedin_person_profile
   - web_data_linkedin_company_profile
   
   These MCP tools allow deep scraping capabilities with content returned in Markdown format, which is suitable for parsing and transformation. The MCP nodes are credentialed using a pre-set API key to authenticate against the Bright Data platform.
   
   Step 4: Data Transformation & Storage
   
   Once data is scraped:
   - it is either parsed using JavaScript code functions (to convert Markdown content to JSON),
   - saved to disk in JSON format (e.g., D:\LinkedIn-Person.json),
   - or sent to a webhook endpoint for testing purposes.
   
   Step 5: AI-Powered Content Enrichment
   
   Rather than just stopping at data collection, this workflow adds another layer using Google Gemini, a powerful generative AI service built on Google’s PaLM API.
   
   The AI model is prompted to analyze and turn company-related JSON data (such as the “about” or “company story” fields) into complete blog posts or descriptive narratives. The node used here is “Google Gemini Chat Model,” configured with the model variant “gemini-2.0-flash-exp.”
   
   This creative content generation transforms raw company metadata into professional text that could be published on websites, CRMs, or sales enablement platforms.
   
   Step 6: Data Aggregation & Output
   
   Post-processing nodes then:
   - Aggregate the structured and AI-enriched content
   - Send this data forward into a Merge node
   - Dispatch it either to a webhook or file storage destination for archiving or further handling
   
   The Merge and Aggregate nodes ensure that both structured data ("about", etc.) and AI-generated narratives ("company_story") coexist in a final combined format.
   
   Real-World Use Cases
   
   This workflow is ideal for a variety of data-centric roles and industries:
   - B2B marketers scraping competitor or lead LinkedIn data
   - HR/recruiting teams collecting candidate insights
   - Business analysts building competitive intelligence dashboards
   - Content writers using AI to generate bio or company write-ups at scale
   - Startups enriching CRM data without paying a premium for data enrichment APIs
   
   No-Code, Scalable Intelligence
   
   The real power of this workflow lies in its scalability and modularity. Using n8n, users can plug in any number of URLs, iterate across batches of LinkedIn profiles, and schedule tasks automatically — all while benefiting from the structured accuracy of Bright Data’s scraping tools and the generative fluency of Google Gemini.
   
   Moreover, output flexibility — writing files, sending data to webhooks, or connecting downstream APIs — means this workflow can neatly integrate into nearly any stack.
   
   Conclusion
   
   By integrating Bright Data MCP’s scraping power with Google Gemini’s generative AI and n8n’s clever automation capabilities, users can build sophisticated LinkedIn data pipelines with minimal coding effort. Whether you're a solo marketer or part of a data-driven enterprise, this automation framework empowers you to extract value from LinkedIn at scale — all while staying in full control of how, when, and what you capture.
   
   With AI at the center and modular components for data scraping, formatting, and publishing, this n8n-based workflow turns LinkedIn into a powerful, automated research and content channel.
   
   Do more with less — and let your automation stack do the heavy lifting.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.