Scryfall Python API Docs | dltHub

Scryfall is a public API that provides detailed Magic: The Gathering card data. The REST API base URL is https://api.scryfall.com and No authentication required; requests must include a User-Agent and Accept header..

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 Scryfall data in under 10 minutes.

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What data can I load from Scryfall?

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

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How do I authenticate with the Scryfall API?

The Scryfall API is public; no API key or bearer token is required. Every request must include a User-Agent header identifying the client and an Accept header specifying the response format.

1. Get your credentials

No credentials are needed; the API is publicly accessible. Simply include the required User-Agent and Accept headers in each request.

2. Add them to .dlt/secrets.toml

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[sources.scryfall_source]

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 Scryfall 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 scryfall_pipeline.py

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

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Pipeline scryfall_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset scryfall_data
The duckdb destination used duckdb:/scryfall.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 scryfall_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 cards_search and catalog_land_types from the Scryfall 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 scryfall_source(=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.scryfall.com",
            "auth": {
                "type": "none",
                "": ,
            },
        },
        "resources": [
            {"name": "cards_search", "endpoint": {"path": "cards/search", "data_selector": "data"}},
            {"name": "catalog_land_types", "endpoint": {"path": "catalog/land-types", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="scryfall_pipeline",
        destination="duckdb",
        dataset_name="scryfall_data",
    )
    load_info = pipeline.run(scryfall_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("scryfall_pipeline").dataset()
sessions_df = data.cards_search.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM scryfall_data.cards_search LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("scryfall_pipeline").dataset()
data.cards_search.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 Scryfall 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

Rate Limits

The API requests should include a 50–100 ms delay between calls. Excessive traffic may trigger a 429 Too Many Requests response.

Common HTTP Errors

• 404 Not Found – The requested resource does not exist.
• 422 Unprocessable Entity – Invalid query parameters or request format.
• 429 Too Many Requests – Rate limit exceeded; back off and respect the delay requirement.

Pagination

When requesting large result sets (e.g., /cards/search), the response may include the header X-Scryfall-Has-More. Use X‑Scryfall‑Next‑Page to fetch the next page of results.

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