Load SpyFu data to DuckDB

SpyFu provides a REST API for accessing competitive search intelligence including keyword research, domain analysis, and SEO data. Everything needed to build a working SpyFu → 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 SpyFu 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 SpyFu 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 SpyFu 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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SpyFu API at a glance

These values come from the SpyFu API documentation. Check them against the vendor's current reference before relying on them in production.

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How do I authenticate with the SpyFu API?

Authentication is performed by passing an API key either as a Bearer token in the 'Authorization' header or as an 'api_key' query parameter.

1. Get your credentials

1. Log in to your SpyFu account at https://www.spyfu.com/account. 2. Navigate to the Account Settings menu, then select the API Usage section (or My API Key page). 3. Your credentials consist of an API ID (username) and a secret key (password). You may need to click to reveal the secret key. Copy these values. Optionally, a Base64-encoded string is also available in this section for basic authorization.

2. Add them to .dlt/secrets.toml

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[sources.spyfu_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 SpyFu data can I load into DuckDB?

These are the SpyFu endpoints dlt can load into DuckDB:

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How do I load only new SpyFu records?

SpyFu exposes startingRow on cloud_ad_history_api/v2/domain/getDomainAdHistory, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "ad_history", "endpoint": {
    "path": "cloud_ad_history_api/v2/domain/getDomainAdHistory",
    "incremental": {"cursor_path": "startingRow", "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 SpyFu pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading getRelatedKeywords and getMostValuableKeywords from the SpyFu API into DuckDB:

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

@dlt.source
def spyfu_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.spyfu.com",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "ad_history", "endpoint": {"path": "cloud_ad_history_api/v2/domain/getDomainAdHistory"}},
            {"name": "top_pages", "endpoint": {"path": "top_pages_api/v2/getTopPages"}}
        ],
    }
    yield from rest_api_resources(config)


def load_spyfu_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="spyfu_pipeline",
        destination="duckdb",
        dataset_name="spyfu_data",
    )
    load_info = pipeline.run(spyfu_source())
    print(load_info)


if __name__ == "__main__":
    load_spyfu_to_duckdb()

Run it with python spyfu_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 SpyFu 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("spyfu_pipeline").dataset()
df = data.ad_history.df()
print(df.head())

SQL:

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SELECT * FROM spyfu_data.ad_history LIMIT 10;

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

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How do I deploy the SpyFu 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 SpyFu 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 SpyFu 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