Load 1040 integrations data to DuckDB

IRS e-Services API is a collection of REST endpoints for tax professionals to submit and retrieve tax data, including 1040 filings. Everything needed to build a working 1040 integrations → 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 1040 integrations 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 1040 integrations 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 1040 integrations 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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1040 integrations API at a glance

These values come from the 1040 integrations API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the 1040 integrations API?

The API uses OAuth 2.0 authentication, requiring a Bearer token. Requests must include the 'Authorization: Bearer <access_token>' header.

1. Get your credentials

To obtain your API credentials for the 1040 Parser API, visit the service dashboard immediately after signing up for an account. Your unique API key is automatically generated and displayed within the dashboard interface upon registration.

2. Add them to .dlt/secrets.toml

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[sources._1040_integrations_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.

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What 1040 integrations data can I load into DuckDB?

These are the 1040 integrations endpoints dlt can load into DuckDB:

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How do I load only new 1040 integrations records?

The 1040 integrations 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.

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{"name": "sor_messages", "endpoint": {
    "path": "esrv/api/sor/messages",
    # 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.

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What does the generated 1040 integrations pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v1/parse and /v1/status from the 1040 integrations API into DuckDB:

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

@dlt.source
def _1040_integrations_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.www4.irs.gov",
            "auth": {"type": "bearer", "token": client_id},
        },
        "resources": [
            {"name": "sor_messages", "endpoint": {"path": "esrv/api/sor/messages", "data_selector": "messages"}},
            {"name": "tilm_requests", "endpoint": {"path": "esrv/api/tinm/request", "data_selector": "requests"}}
        ],
    }
    yield from rest_api_resources(config)


def load__1040_integrations_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="_1040_integrations_pipeline",
        destination="duckdb",
        dataset_name="_1040_integrations_data",
    )
    load_info = pipeline.run(_1040_integrations_source())
    print(load_info)


if __name__ == "__main__":
    load__1040_integrations_to_duckdb()

Run it with python _1040_integrations_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 1040 integrations 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("_1040_integrations_pipeline").dataset()
df = data.sor_messages.df()
print(df.head())

SQL:

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SELECT * FROM _1040_integrations_data.sor_messages LIMIT 10;

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

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How do I deploy the 1040 integrations 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 1040 integrations 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 1040 integrations 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