As an SEO professional or developer, keeping track of how Google replica watches UK indexes your website is crucial. The Google Search Console provides a wealth of data, but manually inspecting URLs one by one can be tedious. Thankfully, the best replica watches Search Console API offers a programmatic way to retrieve this information. In this blog post, I’ll walk you through a Python script I built to automate URL inspections using the cheap replica rolex watches Search Console API, complete with error handling and CSV output. Whether you’re auditing a site or monitoring crawl status, this script can save you time and effort.

What Does the Script Do?

This Python script:

• Uploads a CSV file containing a list of URLs you want to inspect.
• Authenticates with the Google Search Console API using a service account.
• Queries the API to retrieve indexing details for each URL (e.g., coverage state, crawl time, mobile usability).
• Saves the results in a new CSV file for easy analysis.

By the end, you’ll have a downloadable report with key insights about how Google sees your pages.

Prerequisites

Before diving into the code, here’s what you’ll need:

1. Google Cloud Project: Set up a project in the Google Cloud Console and enable the Search Console API.
2. Service Account: Create a service account, download its JSON key file (e.g., api_key.json), and store it in Google Drive.
3. Search Console Access: Add the service account email (e.g., your-service-account@your-project.iam.gserviceaccount.com) as a user in Search Console for the property you’re inspecting.
4. Python Environment: This script is designed for Google Colab, but it can be adapted for other environments. You’ll need libraries like google-auth, google-api-python-client, and pandas.

The Code

Here’s the full script with explanations:

from google.oauth2 import service_account
from googleapiclient.discovery import build
import pandas as pd
from pandas import DataFrame
from google.colab import files
import io
from google.colab import drive
uploaded = files.upload()
# Get the url list from the upload so we can read it into a CSV.
for key in uploaded.keys():
  filename = key
df = pd.read_csv(io.BytesIO(uploaded[filename]))
url_list = df.values.tolist()
number_of_elements = len(url_list)
item_number = 1
#Mount Google Drive First
creds = "/content/drive/api_key.json" #replace with location of script in Google Drive
scopes = [
    'https://www.googleapis.com/auth/webmasters',
    'https://www.googleapis.com/auth/webmasters.readonly'
    ]
credentials = service_account.Credentials.from_service_account_file(creds, scopes=scopes)
service = build('searchconsole','v1',credentials=credentials)
results = []
for i in url_list:
  url = i[0]
  request = {
    'inspectionUrl': url,
    'siteUrl': 'https://example.com/' # Replace with your Search Console property URL
  }
  try:
    response = service.urlInspection().index().inspect(body=request).execute()
    API_result_status = "Success"
    inspectionResult = response['inspectionResult']
    coverageState = inspectionResult['indexStatusResult']['coverageState'].replace("n", " ")
    robotsTxtState = inspectionResult['indexStatusResult']['robotsTxtState']
    indexingState = inspectionResult['indexStatusResult']['indexingState']
    try:
      lastCrawlTime = inspectionResult['indexStatusResult']['lastCrawlTime']
    except:
      lastCrawlTime = "n/a"
    pageFetchState = inspectionResult['indexStatusResult']['pageFetchState']
    try:
      googleCanonical = inspectionResult['indexStatusResult']['googleCanonical']
    except:
      googleCanonical = "n/a"
    try:
      userCanonical = inspectionResult['indexStatusResult']['userCanonical']
    except:
      userCanonical = "n/a"
    try:
      crawledAs = inspectionResult['indexStatusResult']['crawledAs']
    except:
      crawledAs = "n/a"
    try:
      mobileUsabilityResult = inspectionResult['mobileUsabilityResult']['verdict']
    except:
      mobileUsabilityResult = "n/a"
  except:
    print("Failed to read " + str(url) + ". Skipping to next page.")
    API_result_status = "Fail"
    inspectionResult = "n/a"
    coverageState = "n/a"
    robotsTxtState = "n/a"
    indexingState = "n/a"
    lastCrawlTime = "n/a"
    pageFetchState = "n/a"
    googleCanonical = "n/a"
    userCanonical = "n/a"
    crawledAs = "n/a"
    mobileUsabilityResult = "n/a"
  result_url = [url,API_result_status,coverageState,robotsTxtState,indexingState,lastCrawlTime,pageFetchState,googleCanonical,userCanonical,crawledAs,mobileUsabilityResult]
  print(url + ' complete. ' + str(item_number) + ' of ' + str(number_of_elements))
  results.append(result_url)
  item_number = item_number + 1
Feature_Labels = ['url','API_result_status','coverageState','robotsTxtState','indexingState','lastCrawlTime','pageFetchState','googleCanonical','userCanonical','crawledAs','mobileUsabilityResult']
print(result_url)
#Convert to dataframe and download
results_csv = DataFrame (data=results,columns=Feature_Labels)
results_csv.to_csv('Inspect-URL-Results.csv')
files.download('Inspect-URL-Results.csv')

How It Works

1. Setup: The script mounts Google Drive, uploads a CSV file (with a single column of URLs), and reads it into a list.
2. Authentication: It uses a service account JSON key to authenticate with the Search Console API, specifying the necessary scopes.
3. Inspection Loop: For each URL, it sends an inspection request and extracts fields like coverageState (e.g., “Submitted and indexed”) and lastCrawlTime. If a field is missing or the request fails, it defaults to “n/a”.
4. Output: Results are stored in a list, converted to a Pandas DataFrame, and downloaded as a CSV.

Example Usage

1. Prepare a CSV file (e.g., urls.csv) with a column of URLs:
2. Upload it when prompted in Colab.
3. Replace ‘https://example.com/’ with your Search Console property URL.
4. Run the script and download Inspect-URL-Results.csv.

The output might look like this:

Why Use This Script?

Manually checking URLs in Search Console is fine for a handful of pages, but for larger sites, automation is a game-changer. This script lets you:

• Audit indexing status across hundreds of pages.
• Identify crawl issues or canonical mismatches.
• Export data for deeper analysis in tools like Excel or Google Sheets.

Conclusion

The Google Search Console API is a powerful tool, and with a little Python, you can unlock its full potential. This script is a starting point—feel free to adapt it to your needs, whether that’s adding more fields, integrating with other APIs, or building a full SEO dashboard.