Load Bing webmaster tools data to DuckDB

Bing Webmaster Tools is a service for site owners to monitor site health, manage indexing, and submit content to Bing's search index. Everything needed to build a working Bing webmaster tools → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Bing webmaster tools to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Bing webmaster tools to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Bing webmaster tools API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Bing webmaster tools API at a glance

These values come from the Bing webmaster tools API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Bing webmaster tools API?

Requests are authenticated via either an API key passed as a query parameter or an OAuth 2.0 access token provided in the Authorization header as a Bearer token.

1. Get your credentials

1. Sign in to your Bing Webmaster Tools account. 2. Navigate to the Settings menu located in the top right corner of the dashboard. 3. Select the API Access section. 4. If this is your first time accessing this section, review and accept the displayed Terms and Conditions. 5. Click Generate API Key to create your credentials. Note that only one API key can be generated per user, and it applies to all verified sites within that account.

2. Add them to .dlt/secrets.toml

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[sources.bing_webmaster_tools_source]
api_key = "your_api_key_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Bing webmaster tools data can I load into DuckDB?

These are the Bing webmaster tools endpoints dlt can load into DuckDB:

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How do I load only new Bing webmaster tools records?

The Bing webmaster tools API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "get_crawl_issues", "endpoint": {
    "path": "json/GetCrawlIssues",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Bing webmaster tools pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading json/GetCrawlIssues and json/GetQueryData from the Bing webmaster tools API into DuckDB:

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

@dlt.source
def bing_webmaster_tools_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://ssl.bing.com/webmaster/api.svc",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "get_crawl_issues", "endpoint": {"path": "json/GetCrawlIssues"}},
            {"name": "get_query_data", "endpoint": {"path": "json/GetQueryData"}}
        ],
    }
    yield from rest_api_resources(config)


def load_bing_webmaster_tools_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bing_webmaster_tools_pipeline",
        destination="duckdb",
        dataset_name="bing_webmaster_tools_data",
    )
    load_info = pipeline.run(bing_webmaster_tools_source())
    print(load_info)


if __name__ == "__main__":
    load_bing_webmaster_tools_to_duckdb()

Run it with python bing_webmaster_tools_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Bing webmaster tools data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("bing_webmaster_tools_pipeline").dataset()
df = data.get_query_data.df()
print(df.head())

SQL:

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SELECT * FROM bing_webmaster_tools_data.get_query_data LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Bing webmaster tools to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Bing webmaster tools loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Bing webmaster tools data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack