Fancyhands Python API Docs | dltHub

Fancy Hands is a virtual assistant platform that exposes a developer API to create tasks, manage incoming-call phone numbers and conversations, and integrate assistant-driven workflows. The REST API base URL is https://www.fancyhands.com/api and API access requires a consumer key and secret (API key pair) provided in the API Explorer / developer dashboard..

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 Fancyhands data in under 10 minutes.

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What data can I load from Fancyhands?

Here are some of the endpoints you can load from Fancyhands:

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How do I authenticate with the Fancyhands API?

Fancy Hands issues an API consumer key and secret for apps. Requests use those credentials from your app context; the public docs instruct developers to email api@fancyhands.com to get started and obtain the keys.

1. Get your credentials

1. Email api@fancyhands.com describing your use case to request API access.
2. The Fancy Hands team will create an app and supply a consumer key and secret (visible in the API Explorer).
3. Use the provided key/secret in the API Explorer or your app; configure a webhook URL if needed.

2. Add them to .dlt/secrets.toml

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[sources.fancyhands_source]
consumer_key = "your_consumer_key_here"

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 Fancyhands 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 fancyhands_pipeline.py

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

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Pipeline fancyhands_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fancyhands_data
The duckdb destination used duckdb:/fancyhands.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 fancyhands_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 incoming_call_numbers and incoming_call_objects from the Fancyhands 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 fancyhands_source(consumer_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.fancyhands.com/api",
            "auth": {
                "type": "api_key",
                "consumer_key": consumer_key,
            },
        },
        "resources": [
            {"name": "incoming_call_numbers", "endpoint": {"path": "call/number"}},
            {"name": "incoming_call_objects", "endpoint": {"path": "call/incoming"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fancyhands_pipeline",
        destination="duckdb",
        dataset_name="fancyhands_data",
    )
    load_info = pipeline.run(fancyhands_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("fancyhands_pipeline").dataset()
sessions_df = data.incoming_call_numbers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM fancyhands_data.incoming_call_numbers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("fancyhands_pipeline").dataset()
data.incoming_call_numbers.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 Fancyhands 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 have not been issued a consumer key/secret by Fancy Hands (contact api@fancyhands.com) the API Explorer will show you as not logged in and requests will fail. Ensure you use the app consumer key/secret shown in the API Explorer.

Webhook delivery and retries

When configuring webhooks in the API Explorer, Fancy Hands will POST to the webhook and will retry up to 3 times on delivery failure.

Buying phone numbers, billing and errors

Buying a phone number incurs a monthly fee (the price is returned by the Number endpoint). Ensure your account has credits; the Explorer shows credits remaining. If purchase fails, inspect error response from the Number POST for details.

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