Load Fantastical data to DuckDB

Fantastical is a calendar and task management application that provides limited programmatic integration via URL schemes and webhook triggers for supported services like Zapier. Everything needed to build a working Fantastical → 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.

---

Build your Fantastical 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 Fantastical 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 Fantastical 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.

---

Fantastical API at a glance

These values come from the Fantastical API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Fantastical API?

Authentication is performed using an API key generated in the Flexibits Account. The key is required for connecting integrations like Zapier.

1. Get your credentials

To obtain an API key for Fantastical integrations (such as Zapier), log in to your Flexibits Account portal at the official Flexibits website. Navigate to your account settings or the integration section where you can generate a new API key. Note that these keys are typically displayed only once upon generation, so ensure you save it securely immediately. If the key is regenerated or deleted, existing integrations will stop functioning.

2. Add them to .dlt/secrets.toml

Copy
[sources.fantastical_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.

---

What Fantastical data can I load into DuckDB?

These are the Fantastical endpoints dlt can load into DuckDB:

---

How do I load only new Fantastical records?

The Fantastical 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.

Copy
{"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.

---

What does the generated Fantastical pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading webhooks and openings from the Fantastical API into DuckDB:

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

@dlt.source
def fantastical_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Fantastical does not provide a public REST API; integration is primarily handled via app-specific URL schemes or official platform-specific integrations like Zapier.",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "webhooks", "endpoint": {"path": "webhooks"}},
            {"name": "webhook_triggers", "endpoint": {"path": "webhooks/triggers"}}
        ],
    }
    yield from rest_api_resources(config)


def load_fantastical_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fantastical_pipeline",
        destination="duckdb",
        dataset_name="fantastical_data",
    )
    load_info = pipeline.run(fantastical_source())
    print(load_info)


if __name__ == "__main__":
    load_fantastical_to_duckdb()

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

---

How do I query Fantastical 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):

Copy
import dlt

data = dlt.pipeline("fantastical_pipeline").dataset()
df = data.webhooks.df()
print(df.head())

SQL:

Copy
SELECT * FROM fantastical_data.webhooks LIMIT 10;

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

---

How do I deploy the Fantastical 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 Fantastical loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

---

What other destinations can I load Fantastical 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.

---

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