GSC Overdrive: How to Crush Google's 2,000 URL Inspection Limit

The core of the project: bypassing the GSC limit

The main problem is that GSC's URL Inspection tool imposes a strict daily limit of 2,000 URLs per property. For huge enterprise sites with tens of thousands of pages, this limit is a real blocker.

My solution was to create a GSC URL Inspector tool – a Python script designed to bypass this by leveraging GSC's folder-level properties while still working within the product's constraints.

The dynamic property creation hack

The key idea is to treat subfolders as separate GSC properties, effectively granting an unlimited practical daily inspection quota, distributed across multiple properties you control.

1. Pre-analysis: the script takes an input list of URLs and groups them by their parent folders (for example, /blog/ or /products/).
2. Quota segmentation: each folder is matched to an appropriate property, with some smart rules:
   • Minimum threshold (fewer than 100 URLs) – any folder with under 100 URLs is "rolled up" and inspected under the main parent property to avoid creating noisy properties.
   • Standard assignment (100–2,000 URLs) – folders with enough volume get their own folder-level property (for example, https://example.com/products/).
   • The overflow (more than 2,000 URLs) – very large folders (for example, 5,500 URLs in /blog/) are split across multiple numbered properties such as https://example.com/blog/, https://example.com/blog-2/, and https://example.com/blog-3/.
3. Execution: the script iterates through the URL list, sending each URL to the GSC API via its assigned property, ensuring you use as much daily quota as possible in a controlled way.

The essential toolkit I built

To turn this from a clever idea into an industrial-strength SEO audit machine, a few foundational pieces had to be in place.

1. The script and dependencies

• Python 3.8+ runtime.
• A main script file, for example main.py.
• A requirements.txt file listing dependencies such as pandas, google-api-python-client, and google-auth, installed in a dedicated environment.

2. Google Cloud API credentials

To enable the script to communicate with GSC, I set up a Google Cloud project and enabled the Google Search Console API. I then created a new OAuth 2.0 Client ID (Desktop app), and downloaded the JSON file, saving it as client_secret.json. This effectively acts as the private key for the script.

3. The input

The script expects a simple CSV file containing a list of URLs, with a header column named url. From there, everything else is automated.

Robust error handling and resilient execution

An automation like this is only useful if it can run unattended, recover from temporary issues, and avoid corrupt data. That's why I focused heavily on defensive engineering.

• Rate limits: if the Google API returns a 429 "Slow down" response, the script automatically pauses for 60 seconds and retries, rather than failing the whole job.
• Missing properties: if a required folder-level property doesn't exist yet, the script attempts to create it on the fly using the API.
• Interrupted scans: the script regularly saves its progress. If it stops or crashes, you can rerun it and pick up where you left off, avoiding double-counting and partial datasets.

The analysis-ready output

The real value is in the dataset you end up with. The script generates an output_results.csv file – a flattened table that's ready for analysis in your tool of choice.

Key data points extracted from the inspection include:

• The original url and the property_url used for the inspection.
• The most critical metric: indexStatus_coverageState, which gives the exact indexing status (for example, "Indexed, not submitted in sitemap" or "Crawled – currently not indexed").
• indexStatus_robotsTxt and mobileUsability_status (pass/fail).
• The lastCrawlTime for each URL.

What you can analyse with this data

Key takeaways and next steps

It's been quite a journey turning a limited manual check into a scalable SEO audit machine. If you work on large sites and are constantly fighting GSC limits, this kind of approach can shift you from reactive checks to proactive, data-driven decision making.