Load Wp-webhooks data to DuckDB

WP Webhooks (Flow Systems Webhook Actions) is a WordPress plugin that provides a comprehensive REST API for managing webhooks, delivery logs, event queues, and API tokens. Everything needed to build a working Wp-webhooks → 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 Wp-webhooks 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 Wp-webhooks 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 Wp-webhooks 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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Wp-webhooks API at a glance

These values come from the Wp-webhooks API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Wp-webhooks API?

The API uses token-based authentication via the X-FSWA-Token header. Alternatively, developers can use the standard Authorization: Bearer header or the api_token query parameter.

1. Get your credentials

To obtain an API token for the WP Webhooks (Webhook Actions) REST API, follow these steps in your WordPress dashboard: 1. Navigate to the WP Webhooks plugin settings in your WordPress admin panel. 2. Locate the "API Tokens" section or menu. 3. Click the button to create a new token. 4. Assign a descriptive name and select the appropriate scope (e.g., 'read', 'operational', 'full', or 'agent'). 5. Save the token. Ensure you copy the token immediately, as it may not be visible again. You can pass this token in your API requests using the X-FSWA-Token header, an Authorization: Bearer header, or the ?api_token= query parameter.

2. Add them to .dlt/secrets.toml

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[sources.wp_webhooks_source]
api_token = "your_generated_api_token_here"
base_url = "https://your-site.com/wp-json/fswa/v1"

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 Wp-webhooks data can I load into DuckDB?

These are the Wp-webhooks endpoints dlt can load into DuckDB:

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How do I load only new Wp-webhooks records?

The Wp-webhooks 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": "webhooks", "endpoint": {
    "path": "webhooks",
    # 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 Wp-webhooks pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /webhooks and /credentials from the Wp-webhooks API into DuckDB:

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

@dlt.source
def wp_webhooks_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://your-site.com/wp-json/fswa/v1",
            "auth": {"type": "bearer", "token": api_token},
        },
        "resources": [
            {"name": "webhooks", "endpoint": {"path": "webhooks"}},
            {"name": "logs", "endpoint": {"path": "logs"}}
        ],
    }
    yield from rest_api_resources(config)


def load_wp_webhooks_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="wp_webhooks_pipeline",
        destination="duckdb",
        dataset_name="wp_webhooks_data",
    )
    load_info = pipeline.run(wp_webhooks_source())
    print(load_info)


if __name__ == "__main__":
    load_wp_webhooks_to_duckdb()

Run it with python wp_webhooks_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 Wp-webhooks 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("wp_webhooks_pipeline").dataset()
df = data.webhooks.df()
print(df.head())

SQL:

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SELECT * FROM wp_webhooks_data.webhooks LIMIT 10;

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

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How do I deploy the Wp-webhooks 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 Wp-webhooks 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 Wp-webhooks 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