Load DocumentCloud data to DuckDB

DocumentCloud is a platform for hosting, searching, and managing primary source documents and research data. Everything needed to build a working DocumentCloud → 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 DocumentCloud 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 DocumentCloud 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 DocumentCloud 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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DocumentCloud API at a glance

These values come from the DocumentCloud API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the DocumentCloud API?

The API uses JWT-based authentication. Users exchange credentials (username and password) at the MuckRock accounts server for an access token, which must be provided in the 'Authorization' header as 'Bearer <access_token>'.

1. Get your credentials

DocumentCloud does not use static API keys. Instead, you authenticate by exchanging your DocumentCloud username and password for a JWT (JSON Web Token) pair. To obtain credentials: 1) Go to the MuckRock accounts server at https://accounts.muckrock.com/ to manage your account. 2) Send a POST request to the authentication endpoint (typically https://accounts.muckrock.com/api/token/) with your 'username' and 'password' as JSON parameters. 3) You will receive an 'access' token (valid for 5 minutes) and a 'refresh' token (valid for one day). 4) Use the 'access' token in the Authorization header of your API requests as follows: 'Authorization: Bearer <access_token>'. When the token expires, use the 'refresh' token to POST to the refresh endpoint to obtain a new 'access' token.

2. Add them to .dlt/secrets.toml

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[sources.documentcloud_source]
documentcloud_username = "your_username"
documentcloud_password = "your_password"

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 DocumentCloud data can I load into DuckDB?

These are the DocumentCloud endpoints dlt can load into DuckDB:

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How do I load only new DocumentCloud records?

DocumentCloud exposes cursor on api/documents/, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "documents", "endpoint": {
    "path": "api/documents/",
    "data_selector": "results",
    "incremental": {"cursor_path": "cursor", "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 DocumentCloud pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /documents/ and /projects/ from the DocumentCloud API into DuckDB:

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

@dlt.source
def documentcloud_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.www.documentcloud.org/api",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "documents", "endpoint": {"path": "api/documents/", "data_selector": "results"}},
            {"name": "projects", "endpoint": {"path": "api/projects/", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def load_documentcloud_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="documentcloud_pipeline",
        destination="duckdb",
        dataset_name="documentcloud_data",
    )
    load_info = pipeline.run(documentcloud_source())
    print(load_info)


if __name__ == "__main__":
    load_documentcloud_to_duckdb()

Run it with python documentcloud_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 DocumentCloud 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("documentcloud_pipeline").dataset()
df = data.documents.df()
print(df.head())

SQL:

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SELECT * FROM documentcloud_data.documents LIMIT 10;

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

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How do I deploy the DocumentCloud 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 DocumentCloud 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 DocumentCloud 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