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GSC Queries By Page
Pull GSC data table in Queries By Page format
Knowing which search queries bring visitors to each page is foundational for smart content decisions. Google Search Console (GSC) holds that information, yet pulling it out page-by-page is painfully manual inside the native UI. “GSC Queries By Page” fetches that granular view in one click, turning a hidden data trove into an actionable table you can sort, filter, and share. If you’re interested in streamlining your overall content strategy, you might also explore our insights on automating content optimization to maximize efficiency.
How the App Is Built in Moonlit
Input – GSC Site Property
Simply paste the exact property name (e.g., sc-domain:example.com or https://www.example.com/). The app will use the OAuth connection you’ve already set up inside Moonlit to authenticate.
Step 1 – Google Search Console node
Dimensions:
pageandqueryare selected, so every row pairs a URL with the exact search term that triggered an impression or click.Date preset: 90 days balances recency with enough volume for meaningful trends.
Search type: set to
web(you could switch to Image, Video, or News later).The API returns up to 25 000 rows covering four key metrics—impressions, clicks, CTR, and average position—already joined to the two dimensions. This immediately surfaces “striking-distance” keywords, discovery terms you never targeted, and pages that rank but fail to attract clicks.
Output – Pulled GSC Data (table)
The table appears right in the app UI and can be downloaded as CSV, piped into another Moonlit step, or sent to Sheets/BigQuery in a follow-up node you add later.
Component | Action | Key Metrics/Details |
|---|---|---|
Input – GSC Site Property | Paste the exact property name (e.g., sc-domain:example.com) and authenticate via OAuth. | Ensures secure access to your Google Search Console data. |
Google Search Console node | Set dimensions (page, query), date preset (90 days), and search type (web). | Up to 25,000 rows with impressions, clicks, CTR, and average position, joined to both dimensions. |
Output – Pulled GSC Data | Data appears in the app UI; can be downloaded as CSV, piped to another step, or sent to Sheets/BigQuery. | Flexible export and integration options for further analysis or automation. |
Customizing the App
Add filters in the GSC node – limit to a country, device, or regex on queries (e.g.,
query includingRegex "how|what|why") to focus on informational intent.Swap the date preset – pull 16 months of data for annual content audits or narrow to the last 7 days when monitoring a fresh launch.
Chain a Python or Chat Model step – automatically tag queries by intent, flag pages with CTR below benchmark, or generate optimization briefs per page. This approach is akin to the methods we discuss in our post on Building High-Quality AI Content Pipelines to keep human intervention minimal.
Visualize instantly – bolt on a Google Sheets Export or Looker Studio connector for live dashboards the wider team can use.
Handle large sites in chunks – add pagination logic (startRow) to loop through more than 25 000 rows if you manage massive e-commerce catalogs.
