Home > Destinations > ChatGPT > Google Page Speed

Connect Google Page Speed to ChatGPT

Connect Google Page Speed to ChatGPT and ask about any metrics or dimensions using current integrations data. No CSV exports or manual campaign summaries.

Looker Studio
Power BI
Google Sheets
BigQuery
Snowflake

Trusted by marketing teams that talk to data everyday

How to connect Google Page Speed to ChatGPT ?

Three steps to your first source-aware prompt.

Step one

Authorize the Google Page Speed account in Catchr

Select Google Page Speed, sign in, and choose the client or business accounts you want to connect.

Client A · Connected sources
5 sources ready
Meta Ads 3 advertising accounts Connected
Google Ads 2 advertising accounts Connected
Google Analytics 4 1 web property Connected
HubSpot 1 CRM portal Connected
Step two

Add the Catchr MCP from official ChatGPT store

Configure the Catchr remote MCP connection in ChatGPT using the connector details from Catchr.

Step three

Choose, adapt, and run a prompt.

Replace account, period, KPI target, and threshold variables before you ask ChatGPT.

A simple client question should not trigger an hour of data prep.

“Why did CPA rise?” sounds simple. The answer is usually buried across exports, formulas, filters, and tabs before the analysis can even start.

Pull another CSV

Choose the right account, date range, breakdowns, and fields. Then wait for the file and do it again when the question changes.

Rebuild the context

Rename columns, fix formats, join tabs, and check formulas before you can trust the comparison.

Summarize it by hand

Turn the numbers into a client-ready answer, then repeat the whole process for the inevitable follow-up.

Ask the account. Get to the explanation.

Catchr MCP gives ChatGPT access to the marketing accounts you select, so each follow-up starts with the same marketing context instead of a new manual export.

The hard way

Prepare data before every question.

  • Export files from the marketing platform
  • Reformat and reconcile columns
  • Rebuild the analysis for each follow-up
The Catchr way

Connect once. Keep asking.

  • Select the right accounts and data
  • Ask in the language your team already uses
  • Investigate with follow-up questions

Twelve Google Page Speed prompts for analyzing real account data.

Choose your role, open a workflow, then replace the bracketed variables before pasting the prompt into ChatGPT. Every prompt asks for evidence, limits, and a concrete output.

Agency promptCopy prompt
Cross-Client Page Speed Morning Triage
Using the Catchr MCP, pull the latest Google Page Speed data for [URL/Site] across [List of Client Sites], split by Page ID and Device. Review Performance score, Largest Contentful Paint, Cumulative Layout Shift, First Contentful Paint, Total Blocking Time, Speed Index, Server Response Time, and Total Byte Weight against [Client Thresholds]. Label each client page Healthy, Watch, or Critical, rank the pages that need attention today, and give one evidence-based next action per flagged page. Keep Desktop and Mobile findings separate and do not infer a cause unless a confirmed metric supports it.
Client-Ready Web Performance Review
Using the Catchr MCP, pull Google Page Speed data for [URL/Site] over [Reporting Period] and [Comparison Period]. Build a client-ready review by Page ID and Device showing Performance score, Accessibility score, Best Practices score, SEO score, Largest Contentful Paint, Cumulative Layout Shift, First Contentful Paint, Total Blocking Time, and Speed Index. Quantify each change, highlight wins and regressions, explain the likely business impact in plain English, and finish with three prioritized recommendations, their supporting metrics, and the metric to recheck after implementation.
Portfolio Regression and Root-Cause Finder
Using the Catchr MCP, analyze [URL/Site] for [List of Client Sites] over [Analysis Period], using Date or Extracted Date to detect recent regressions by Page ID and Device. For each decline in Performance score or Core Web Vitals available in Catchr, trace supporting signals across Largest Contentful Paint, Cumulative Layout Shift, First Input Delay, Max Potential FID, Total Blocking Time, Mainthread Work Time, Render Blocking Resources Time, Server Response Time, Redirects Time, Network Round-Trip Time, and Total Byte Weight. Separate confirmed observations from hypotheses, rank issues by severity and client impact, and produce an agency action queue with the specialist to involve and the validation metric for each fix.
Daily Client Site Priority Queue
Using the Catchr MCP, pull the latest Google Page Speed data for [URL/Site] across [List of Client Sites] and compare it with [Recent Baseline]. By Page ID and Device, review Performance score, Largest Contentful Paint, Cumulative Layout Shift, Total Blocking Time, Server Response Time, and Total Byte Weight. Rank clients by urgency, explain each regression in one clear sentence using only measured evidence, and provide one task that fits [Available Time Today] plus the exact metric and threshold to recheck after the task.
Monthly Website Performance Report
Using the Catchr MCP, pull Google Page Speed results for [URL/Site] for [Reporting Month] and [Previous Month], grouped by Page ID and Device. Summarize changes in Performance score, Accessibility score, Best Practices score, SEO score, Largest Contentful Paint, Cumulative Layout Shift, First Contentful Paint, Total Blocking Time, Speed Index, and Time to Interactive. Write a client-ready report in plain English covering what improved, what regressed, the metrics that support each conclusion, any limitations in the available data, and three concrete priorities for [Next Month].
Evidence-Based Developer Handoff
Using the Catchr MCP, diagnose the weakest page on [URL/Site] over [Period], comparing Mobile and Desktop for each Page ID. Use Largest Contentful Paint, Cumulative Layout Shift, First Input Delay, Max Potential FID, Total Blocking Time, Mainthread Work Time, Bootup Time, Render Blocking Resources Time, Redirects Time, Server Response Time, Network Round-Trip Time, and Total Byte Weight to narrow the problem. Produce a concise developer handoff with observed values, affected device, reproduction scope, ranked hypotheses clearly labeled as hypotheses, recommended checks, acceptance criteria, and the Catchr metrics to monitor after deployment.
Storefront Speed and Conversion Risk Check
Using the Catchr MCP, pull the latest Google Page Speed results for [URL/Site], covering [Homepage, Collection, Product, Cart, and Checkout URLs] where available. Compare Mobile and Desktop by Page ID using Performance score, Largest Contentful Paint, Cumulative Layout Shift, First Contentful Paint, Total Blocking Time, Speed Index, and Time to Interactive. Rank the pages most likely to create shopping friction based on the measured severity, state exactly which metric is responsible, and recommend the first technical or merchandising action to test for each page without claiming conversion impact that the data does not measure.
Product Launch Performance Guardrail
Using the Catchr MCP, monitor [URL/Site] over [Launch Window] versus [Pre-Launch Baseline], grouped by Date, Page ID, and Device. Focus on launch landing pages and product pages, and compare Performance score, Largest Contentful Paint, Cumulative Layout Shift, Total Blocking Time, Total Byte Weight, Server Response Time, and Render Blocking Resources Time. Flag any regression beyond [Threshold], identify whether the evidence points to heavier content, server latency, blocking resources, or main-thread pressure, and return a launch-day action plan with severity, owner, immediate mitigation, and the metric to verify recovery.
Mobile Store Optimization Backlog
Using the Catchr MCP, analyze Mobile Google Page Speed data for [URL/Site] over [Period] and rank Page IDs by optimization priority. Score each page using Performance score, Largest Contentful Paint, Cumulative Layout Shift, First Contentful Paint, Total Blocking Time, Mainthread Work Time, Bootup Time, Total Byte Weight, and Server Response Time. Group the measured issues into loading, visual stability, responsiveness, JavaScript execution, network, or page-weight themes; then create a backlog of specific fixes ordered by impact and effort, with a baseline value and success threshold for every recommendation.
Page Speed Data Quality Audit
Using the Catchr MCP, audit Google Page Speed data for [URL/Site] over [Period] by Date, Extracted Date, Page ID, and Device. Test for missing dates, duplicate URL-device-date keys, null-heavy metrics, impossible negative values, score values outside their expected scale, abrupt unit-like changes, and pages or devices with incomplete coverage. Include Performance score, Accessibility score, Best Practices score, SEO score, Largest Contentful Paint, Cumulative Layout Shift, Total Blocking Time, and Total Byte Weight in the checks. Return an exception table with the failed rule, observed value, severity, affected records, and a reproducible remediation or validation step.
Ninety-Day Web Performance Outlier Monitor
Using the Catchr MCP, pull at least [Lookback Period, e.g., 90 days] of Google Page Speed data for [URL/Site], grouped by Date, Page ID, and Device. For Performance score, Largest Contentful Paint, Cumulative Layout Shift, First Contentful Paint, Total Blocking Time, Speed Index, Server Response Time, Mainthread Work Time, and Total Byte Weight, compare each observation with its trailing [Baseline Window, e.g., 30-day] mean and standard deviation. Flag deviations beyond [Z-score Threshold, e.g., 2] only when [Minimum Observation Threshold] is met, classify each as likely data-quality issue or genuine performance change, and show the supporting values and next investigation.
Device and Metric Reconciliation Dashboard
Using the Catchr MCP, analyze [URL/Site] over [Period] and build a reconciliation view by Page ID, Date, and Device. Verify URL coverage and compare Mobile versus Desktop across Performance score, Largest Contentful Paint, Cumulative Layout Shift, First Input Delay, Max Potential FID, Total Blocking Time, Time to Interactive, Speed Index, Network Round-Trip Time, Server Response Time, Render Blocking Resources Time, and Total Byte Weight. Report coverage gaps, persistent device deltas beyond [Tolerance], contradictory signals, and changes in metric relationships over time. Finish with a documented set of monitoring rules, alert thresholds, and caveats, noting that Catchr exposes FID-related fields but not INP for this connector.
Replace [variables] Facts ≠ hypotheses Action + follow-up metric

Google Page Speed to ChatGPT FAQs

Clear answers for marketers comparing connectors, data sources, reporting workflows, and dashboard setup.

What Google Page Speed data can ChatGPT analyze through Catchr?

ChatGPT can query connected Google Page Speed data for search demand, visibility, query, page, crawl, index, and site-performance reporting.

Representative measures include Performance score, SEO score, First Contentful Paint, Largest Contentful Paint, and Cumulative Layout Shift. Useful breakdowns include Page ID, Device, Date, Year month, and Week of the year. The exact field set depends on the selected dataset and compatible reporting reporting level.

How granular can Google Page Speed analysis be in ChatGPT?

The available detail depends on the fields returned for the selected dataset. Useful breakdowns include Page ID, Device, Date, Year month, and Week of the year.

Ask ChatGPT to state the reporting level it used and keep incompatible levels separate before calculating totals, rates, or comparisons.

Can ChatGPT find search and content opportunities in Google Page Speed?

Yes. ChatGPT can surface queries, pages, topics, or regions with meaningful demand and room to improve. Relevant fields include Performance score, SEO score, First Contentful Paint, Largest Contentful Paint, and Cumulative Layout Shift.

Set the business target, comparison period, and minimum volume before requesting priorities or recommendations.

Can ChatGPT detect technical or visibility issues in Google Page Speed?

Yes. ChatGPT can identify visibility, click-through, crawl, index, or page-performance changes that need investigation. Useful breakdowns include Page ID, Device, Date, Year month, and Week of the year.

Ask it to separate observed data, possible explanations, and the next validation step so an unusual value is not presented as a proven cause.

What types of prompts work best for Google Page Speed analysis?

Strong prompts name the action, account or entity, date range, comparison, and KPI or threshold.

  • Monitor: “Ninety-Day Web Performance Outlier Monitor”
  • Diagnose: “Storefront Speed and Conversion Risk Check”
  • Find opportunities: “Mobile Store Optimization Backlog”
  • Report: “Client-Ready Web Performance Review”

Can I combine Google Page Speed with other data sources in ChatGPT?

Yes, when the other sources are also connected to Catchr. Useful combinations include analytics, advertising, content, commerce, or CRM sources.

Align dates, currencies, identifiers, and definitions first. If the sources cannot be joined reliably, ask for a side-by-side comparison rather than a single attributed result.

Can I analyze multiple sites, properties, domains, or search terms together?

Yes, provided each one is connected and available through Catchr MCP. Ask for separate results first, then create the combined view.

Specify the site or property, country, device, search type, period, and comparison. This keeps one large entity or a definition mismatch from distorting the comparison.

What do I need to connect Google Page Speed to ChatGPT with Catchr?

You need access that can authorize and view the relevant Google Page Speed data, a Catchr workspace with the source connected, and Catchr MCP enabled in ChatGPT.

After selecting the required entities, you can ask questions in natural language without preparing a new CSV export for each analysis.

How recent is the Google Page Speed data, and how much history can I analyze?

ChatGPT analyzes the records returned through the connected Catchr source; it should not assume the data is real time. Freshness and historical coverage can vary by dataset, field, selected period, and source API limits.

Ask it to state the latest query, page, crawl, performance, or extraction date, together with the timezone, requested period, and any missing intervals before interpreting a trend.

Can ChatGPT change anything in Google Page Speed through Catchr?

No. Catchr MCP provides Google Page Speed data for analysis; it does not give ChatGPT permission to change site content, indexing, technical configuration, or source settings.

Use the result to prepare an action plan, then make operational changes in Google Page Speed. Review the supporting figures before acting on the recommendation.

How much does it cost to connect marketing data to ChatGPT with Catchr?

Catchr MCP is included in every standard plan—there is no separate fee for the ChatGPT integration. The Starter plan begins at $20 per month when billed annually, or $24 when billed monthly.

It includes 3 platforms, 10 accounts, unlimited users, and unlimited requests. Larger plans increase the number of platforms and accounts available. See Catchr pricing

‍Can I try the ChatGPT integration before choosing a plan?

Yes. Catchr offers a 14-day free trial with no credit card required.

You can use the trial to connect your marketing sources, authorize Catchr MCP in ChatGPT, and test questions using your own data before subscribing.

Still have a question ? 

Our teams is always here to responds to any question you could have about our data connector. 

Ask the account question before building another export.

Connect yout marketing platform to ChatGPT with Catchr MCP and start investigating current campaign performance.

14 days free-trial
No credit-card required
100+ sources