Google Search Console Python API Docs | dltHub

Google Search Console API is a REST API that provides programmatic access to Search Console features: query search analytics, list/manage verified sites and sitemaps, and inspect URL index status. The REST API base URL is Primary: https://www.googleapis.com/webmasters/v3 (Webmasters/Search Analytics & Sitemaps & Sites) URL Inspection (newer): https://searchconsole.googleapis.com/v1 and OAuth2 (Bearer token) required for authenticated endpoints; use OAuth client credentials and user consent to obtain access tokens..

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 pip install "dlt[workspace]" 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?

The API uses OAuth 2.0. Requests must include an Authorization: Bearer <ACCESS_TOKEN> header. Use the OAuth 2.0 client ID/secret in Google Cloud Console and request appropriate scopes.

1. Get your credentials

1. Open Google Cloud Console (https://console.cloud.google.com). 2) Create or select a project. 3) Enable the Search Console API (searchconsole.googleapis.com or webmasters.googleapis.com). 4) In OAuth consent screen configure application and publish (internal/external). 5) Create OAuth 2.0 Client ID credentials (Application type: Web application/Other). 6) Note client_id and client_secret. 7) Use an OAuth flow (installed app or web) to obtain access and refresh tokens, requesting scopes like https://www.googleapis.com/auth/webmasters or https://www.googleapis.com/auth/webmasters.readonly and for URL Inspection the same webmasters scope. 8) Store refresh token and exchange for access tokens as needed.

2. Add them to .dlt/secrets.toml

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[sources.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 venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt 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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dlt ai toolkit rest-api-pipeline install

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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python search_console_pipeline.py

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

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Pipeline search_console_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset search_console_data
The duckdb destination used duckdb:/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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dlt pipeline search_console_pipeline 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 search_console_source(oauth2_credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Primary: https://www.googleapis.com/webmasters/v3 (Webmasters/Search Analytics & Sitemaps & Sites)
URL Inspection (newer): https://searchconsole.googleapis.com/v1",
            "auth": {
                "type": "oauth2",
                "access_token": oauth2_credentials,
            },
        },
        "resources": [
            {"name": "sites", "endpoint": {"path": "sites", "data_selector": "siteEntry"}},
            {"name": "search_analytics", "endpoint": {"path": "sites/{siteUrl}/searchAnalytics/query", "data_selector": "rows"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="search_console_pipeline",
        destination="duckdb",
        dataset_name="search_console_data",
    )
    load_info = pipeline.run(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("search_console_pipeline").dataset()
sessions_df = data.search_analytics.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM search_console_data.search_analytics LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("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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Troubleshooting

Authentication failures

If you get 401 Unauthorized or 403 Forbidden: ensure you obtained an OAuth2 access token and include it in Authorization: Bearer . Verify the token has scopes https://www.googleapis.com/auth/webmasters or https://www.googleapis.com/auth/webmasters.readonly. Refresh expired tokens.

Permission errors (403)

403 can indicate insufficient permissions for the requested site/property. The account used must be a verified owner or have appropriate permissions in Search Console for that property.

Rate limits and quota

Google APIs use per-project quotas; you may receive 429 Too Many Requests or 403 with rate-limit details. Check Cloud Console Quotas for the Search Console API and implement exponential backoff and retries.

Pagination quirks

Most GET list methods (sites, sitemaps) return arrays in a field (siteEntry, sitemap) and may include nextPageToken for some collections; implement handling for nextPageToken when present.

Common error formats

Errors return standard Google JSON error envelope with HTTP status codes (4xx/5xx) and body {"error": {"code": , "message": "...", "errors": [...]}}. Parse the message and errors[].

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

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

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

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• 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