Parabola Python API Docs | dltHub

Parabola is a no-code data automation platform that provides a specific MCP (Model Context Protocol) server for LLM integration rather than a public REST API for general data extraction. The REST API base URL is https://parabola.io/api/mcp and No public REST API for general use; MCP server uses OAuth 2.0..

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 add "dlt[hub]" and start loading Parabola data in under 10 minutes.

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What data can I load from Parabola?

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

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How do I authenticate with the Parabola API?

Parabola does not offer a general-purpose public REST API for data extraction; it acts as a client for other services. For the specific MCP API, authentication is handled via OAuth 2.0.

1. Get your credentials

1. Sign in to the third-party service (the upstream API) you intend to connect to via Parabola. 2. Follow that service's developer documentation to generate your API credentials (e.g., API key, Client ID/Secret for OAuth). 3. In the Parabola dashboard, open your Flow and add a 'Pull from API', 'Enrich with API', or 'Send to API' step. 4. Within that step's configuration panel, navigate to the 'Authentication' section. 5. Choose the appropriate authentication method (e.g., Bearer Token, Basic, or OAuth2) based on the upstream service's requirements. 6. Paste your credentials into the corresponding fields provided in the Parabola step configuration. 7. If required by the service, add any necessary headers in the 'Advanced Settings' or 'Request Headers' section. 8. Click 'Authorize' to save the account settings.

2. Add them to .dlt/secrets.toml

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[sources.parabola_source]
api_key = "your_api_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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 Parabola 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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uv run python parabola_pipeline.py

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

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Pipeline parabola_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset parabola_data
The duckdb destination used duckdb:/parabola.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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uv run dlthub 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 Pull from API and Enrich with API from the Parabola 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 parabola_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://parabola.io/api/mcp",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "custom_api_resource_1", "endpoint": {"path": "/v1/resource_path_1"}},
            {"name": "custom_api_resource_2", "endpoint": {"path": "/v1/resource_path_2"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="parabola_pipeline",
        destination="duckdb",
        dataset_name="parabola_data",
    )
    load_info = pipeline.run(parabola_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("parabola_pipeline").dataset()
sessions_df = data.custom_api_resource_1.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM parabola_data.custom_api_resource_1 LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("parabola_pipeline").dataset()
data.custom_api_resource_1.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 Parabola 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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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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