Google Search Console Python API Docs | dltHub

Google Search Console API provides programmatic access to search analytics, site/sitemap management, and URL inspection services. The REST API base URL is https://www.googleapis.com/webmasters/v3 (primary); https://searchconsole.googleapis.com/v1 (URL Inspection) and all requests require a Bearer token via OAuth 2.0.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv add "dlt[hub]" and start loading Google Search Console data in under 10 minutes.

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What data can I load from Google Search Console?

Here are some of the endpoints you can load from Google Search Console:

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How do I authenticate with the Google Search Console API?

All requests must include an Authorization: Bearer <ACCESS_TOKEN> header, obtained via an OAuth 2.0 flow using Google Cloud credentials.

1. Get your credentials

To obtain credentials for the Google Search Console API, follow these steps in the Google Cloud Console: 1. Create or select a project. 2. Navigate to APIs & Services > Library, search for 'Google Search Console API', and enable it. 3. Navigate to APIs & Services > OAuth consent screen and configure your app (internal or external). 4. Go to APIs & Services > Credentials, click 'Create Credentials', and select 'OAuth client ID'. 5. Set 'Application type' to 'Web application' (or 'Desktop' for local scripts), add necessary redirect URIs, and save. 6. Copy the Client ID and Client Secret. 7. Use an OAuth flow to exchange these for a refresh token, which is required for automated dlt pipelines. For service-to-service automation without manual user consent, create a 'Service Account' under IAM & Admin instead, download the JSON key file, and add the service account email as a user with 'Restricted' access in the Google Search Console property settings.

2. Add them to .dlt/secrets.toml

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[sources.google_search_console_source]
client_id = "YOUR_CLIENT_ID"
client_secret = "YOUR_CLIENT_SECRET"
refresh_token = "YOUR_REFRESH_TOKEN"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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uv run dlthub ai toolkit install rest-api-pipeline

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Google Search Console API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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uv run python google_search_console_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline google_search_console_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset google_search_console_data
The duckdb destination used duckdb:/google_search_console.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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uv run dlthub show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads sites and search_analytics from the Google Search Console API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def google_search_console_source(oauth2_credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.googleapis.com/webmasters/v3 (primary); https://searchconsole.googleapis.com/v1 (URL Inspection)",
            "auth": {"type": "bearer", "token": oauth2_credentials},
        },
        "resources": [
            {"name": "search_analytics", "endpoint": {"path": "sites/{siteUrl}/searchAnalytics/query", "data_selector": "rows"}},
            {"name": "sites", "endpoint": {"path": "sites", "data_selector": "siteEntry"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="google_search_console_pipeline",
        destination="duckdb",
        dataset_name="google_search_console_data",
    )
    load_info = pipeline.run(google_search_console_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("google_search_console_pipeline").dataset()
sessions_df = data.search_analytics.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM google_search_console_data.search_analytics LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("google_search_console_pipeline").dataset()
data.search_analytics.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Google Search Console data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack