This article provides a complete, practical walkthrough of the Splitout Webhook Create 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 SEO Keyword Research with n8n and Google Autocomplete
   
   Meta Description:
   Discover how to automate keyword research for SEO using this n8n workflow that taps into Google’s autocomplete feature. Learn how it works from webhook trigger to final keyword output—optimized and ready for analysis.
   
   Keywords:
   SEO automation, keyword research, n8n workflow, Google Autocomplete API, Google Suggest API, automated SEO tools, keyword ideas, n8n SEO, workflow automation, XML keyword extraction
   
   Third-Party APIs Used:
   
   - Google Autocomplete API (https://google.com/complete/search?output=toolbar)
   
   Article:
   
   Automating SEO Keyword Research with n8n and Google Autocomplete
   
   For digital marketers and content creators, finding the right keywords is a critical yet time-consuming step for crafting SEO-friendly content. However, with workflow automation tools like n8n, much of this manual labor can be automated—letting creators focus on what they do best: creating. In this article, we’ll explore an n8n workflow designed to streamline the process of generating keyword ideas using Google Autocomplete.
   
   This low-code automation not only accelerates keyword research but also structures the result in a clean, usable format you can feed into your SEO tool or content strategy.
   
   Overview of the Workflow
   
   The goal of this workflow is to receive a keyword input—such as “keyword research”—and return a cleaned list of suggested keyword variations sourced directly from Google Autocomplete results. Here’s how the process unfolds step by step:
   
   1. Webhook Trigger: Receive the Keyword
   The workflow begins by listening for incoming webhook requests. This is handled by the “Receive Keyword” node, which serves as the trigger for the automation. You can call the webhook using a simple GET request like so:
   
   http://localhost:5678/webhook/76a63718-b3cb-4141-bc55-efa614d13f1d?q=keyword%20research
   
   By changing the value of the q parameter, you're effectively sending a new seed keyword into the workflow.
   
   2. HTTP Request: Tap into Google Autocomplete
   Once the keyword query is received, the “Autogenerate Keywords” node kicks in. This node makes an HTTP request to Google’s unofficial autocomplete endpoint:
   
   https://google.com/complete/search?output=toolbar&gl=US&q={{ $json.query.q }}
   
   This API responds with XML-formatted suggestions based on the input keyword—exactly what you see when typing in Google's search bar.
   
   3. XML Parser: Format the Suggestions
   Google returns the autocomplete data in XML format. The “Format Keywords” node reads this XML response and converts it into a structured JSON format that can be more easily processed in n8n.
   
   4. Split Keywords: Normalize the Data
   Next, the “Split Out” node breaks down the ‘CompleteSuggestion’ array into individual suggestions, preparing them for further operations.
   
   5. Clean Keywords: Extract the Data
   Each suggestion from the array contains nested data. The “Clean Keywords” node isolates the actual keyword text from the suggestion object using:
   
   ={{ $json.suggestion.data }}
   
   This step transforms each object into a usable plain string, aligning all keyword entries into a consistent structure.
   
   6. Aggregate Keywords: Collect and Organize
   To wrap up the processing, the “Aggregate” node compiles all the cleaned keyword entries into a single array field called "Keywords". This is useful if you plan to push the data into a Google Sheet, Airtable, or further analytics.
   
   7. Respond to Webhook: Return the Final Result
   The final node, “Return Keywords,” sends the compiled list right back to the user through the webhook response. In essence, the input-output transformation is complete: from one seed keyword to an actionable list of suggestions.
   
   How to Use This Workflow in Practice
   
   Once deployed on your local or cloud-hosted instance of n8n, all you need to do is make a GET request to the webhook URL, optionally replacing “keyword research” with any topic of interest.
   
   Example:
   
   http://localhost:5678/webhook/76a63718-b3cb-4141-bc55-efa614d13f1d?q=digital%20marketing
   
   The workflow will return a structured JSON array of related keyword ideas such as:
   
   - digital marketing strategy
   - digital marketing course
   - digital marketing tools
   - digital marketing agency
   
   …and so on.
   
   Benefits and Use Cases
   
   - Time-saving: No more manually typing in keywords into Google to see suggestions.
   - Scalable: Easily adapt this to batch process multiple keywords or update hourly.
   - Integratable: Plug into Google Sheets, Notion, or email tools to use this as part of larger automation chains.
   - SEO Friendly: Gain insight into long-tail search ideas favored by Google’s algorithm.
   
   Limitations and Considerations
   
   While Google’s autocomplete is a goldmine of search intent data, remember that it’s not an officially documented public API. Although widely used, there’s always a risk that Google could change this endpoint. If your use case depends on stability, explore paid APIs like Moz, SEMrush, or Ahrefs.
   
   In addition, since the tool uses scraping-like behavior (even though it uses a public endpoint), you should respect any usage limitations and avoid excessive requests in a short time frame.
   
   Conclusion
   
   With just a handful of nodes in n8n, you can build a powerful keyword research engine that taps into Google’s own suggestions. This no-code workflow takes just minutes to deploy but can produce valuable SEO insights that would take hours to gather manually. Whether you’re an SEO agency, freelancer, or content strategist, automating keyword research can give you the speed and efficiency edge you need in today’s competitive content market.
   
   So why not automate the grunt work, and focus on what matters most—creating content that ranks.

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.