Click2mail Python API Docs | dltHub

Click2Mail is an API that enables customers to create, manage, and mail printed documents programmatically. The REST API base URL is https://rest.click2mail.com/molpro and All requests use HTTP Basic authentication with a username and password..

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 Click2mail data in under 10 minutes.

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What data can I load from Click2mail?

Here are some of the endpoints you can load from Click2mail:

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How do I authenticate with the Click2mail API?

Authentication is performed via HTTP Basic Auth by supplying the username and password in the request (e.g., requests.get(..., auth=(user, pass))). The Accept header should be set to application/xml to receive XML responses.

1. Get your credentials

1. Log in to your Click2Mail account at https://www.click2mail.com.
2. Open the "API" or "Developer" section in the account dashboard.
3. Request API access for the desired environment (staging or production).
4. The portal will generate a username and password for HTTP Basic authentication.
5. Save these credentials for use in your dlt configuration.

2. Add them to .dlt/secrets.toml

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[sources.click2mail_source]
username = "your_username"
password = "your_password"

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 Click2mail 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 click2mail_pipeline.py

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

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Pipeline click2mail_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset click2mail_data
The duckdb destination used duckdb:/click2mail.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 click2mail_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 jobs and account_addresses from the Click2mail 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 click2mail_source(username=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://rest.click2mail.com/molpro",
            "auth": {
                "type": "http_basic",
                "password": username,
            },
        },
        "resources": [
            {"name": "jobs", "endpoint": {"path": "jobs"}},
            {"name": "account_addresses", "endpoint": {"path": "account/addresses"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="click2mail_pipeline",
        destination="duckdb",
        dataset_name="click2mail_data",
    )
    load_info = pipeline.run(click2mail_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("click2mail_pipeline").dataset()
sessions_df = data.jobs.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM click2mail_data.jobs LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("click2mail_pipeline").dataset()
data.jobs.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 Click2mail 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 errors

• 401 Unauthorized – Invalid username or password.
• 403 Forbidden – Access to the requested resource is denied.

General HTTP errors

• 400 Bad Request – Required parameters are missing or malformed.
• 404 Not Found – The requested endpoint does not exist.
• 500 Internal Server Error – Server‑side problem.

Rate limiting

• The documentation does not specify explicit rate limits; callers should implement exponential backoff on 429 Too Many Requests responses if they occur.

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