N8N WORKFLOW SETUP
Add Google PageSpeed Insights reporting to an n8n workflow
Connect Adzviser to an AI Agent, test one request, and only then add the schedule, alert, or delivery step.
Choose what you want to research
Google PageSpeed Insights is public data, so you can begin without signing in to a source account.
Configure the MCP Client Tool
Add n8n's MCP Client Tool to an AI Agent, enter Adzviser's server address and credentials, and test the tool before activating the workflow.
https://mcp.adzviser.com/httpTest before activating
Run the workflow manually with one Google PageSpeed Insights request. Check the selected account, dates, and output before turning on its trigger.
AUTOMATION BLUEPRINT
Build a repeatable Google PageSpeed Insights reporting workflow
Keep the automation easy to audit: one trigger, one clearly scoped analysis, and one useful destination for the result.
Decide when it runs
Use a schedule, webhook, or manual trigger and calculate a complete reporting period before requesting data.
See a related Google PageSpeed Insights promptGive the AI Agent a stable instruction
Fix the account-selection rule, metrics, breakdowns, comparison, and output format so each run is consistent.
See a related Google PageSpeed Insights promptSend a result people can use
Route the takeaway and evidence table to email, Slack, a database, or the next approved step in your workflow.
See a related Google PageSpeed Insights promptN8N AI AGENT INSTRUCTION
Use a structured Google PageSpeed Insights instruction in every run
A predictable output makes the automation easier to test, route, and monitor.
Compare Core Web Vitals and scores across desktop and mobile.
Use Google PageSpeed Insights for the subject and scope I provide. Then complete this analysis: Analyze the Google PageSpeed Insights data for the URL I provide, comparing Device type = desktop versus mobile. For each device, show Performance score, Largest contentful paint (LCP), Interaction to next paint (INP), Cumulative layout shift (CLS), and Time to first byte (TTFB) with their assessments. Quantify the gap between desktop and mobile and explain which experience needs the most urgent attention. Return valid JSON with keys summary, evidence, alerts, and next_steps so the following n8n node can route the result reliably.
