Clicksend Python API Docs | dltHub

ClickSend is a cloud messaging platform and REST API for sending and receiving SMS, MMS, email, voice and related messaging services. The REST API base URL is https://rest.clicksend.com/v3 and all requests require HTTP Basic authentication (username + API key).

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 Clicksend data in under 10 minutes.

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What data can I load from Clicksend?

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

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How do I authenticate with the Clicksend API?

ClickSend uses HTTP Basic auth where the username is your API username and the password is your API key (unique key). The Authorization header must be: Authorization: Basic Base64(username:password). Also include Content-Type: application/json for JSON requests.

1. Get your credentials

1. Sign into ClickSend dashboard (https://dashboard.clicksend.com). 2) Click "API Credentials" or visit Account / API Credentials. 3) Copy your API username and API key (Unique Key). 4) Use the username as the auth username and the API key as the password in Basic auth (or place in dlt secrets as below).

2. Add them to .dlt/secrets.toml

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[sources.clicksend_source]
username = "your_clicksend_username"
password = "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 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 Clicksend 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 clicksend_pipeline.py

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

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Pipeline clicksend_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset clicksend_data
The duckdb destination used duckdb:/clicksend.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 clicksend_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 sms and mms from the Clicksend 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 clicksend_source(username=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://rest.clicksend.com/v3",
            "auth": {
                "type": "http_basic",
                "password": username,
            },
        },
        "resources": [
            {"name": "sms", "endpoint": {"path": "sms/history", "data_selector": "data.data"}},
            {"name": "mms", "endpoint": {"path": "mms/history", "data_selector": "data.data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="clicksend_pipeline",
        destination="duckdb",
        dataset_name="clicksend_data",
    )
    load_info = pipeline.run(clicksend_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("clicksend_pipeline").dataset()
sessions_df = data.sms.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM clicksend_data.sms LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("clicksend_pipeline").dataset()
data.sms.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 Clicksend 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 failures

If you receive 401/response_code MISSING_CREDENTIALS or INVALID_CREDENTIALS, verify you are using HTTP Basic auth with username (API username) and password (API key). Ensure Authorization header is "Basic <base64(username:password)>" and Content-Type: application/json.

Rate limiting (429)

ClickSend returns 429 Too Many Requests when rate limits are exceeded. Implement exponential backoff and respect the per-endpoint limits. Paginate requests (limit parameter) to reduce load.

Pagination quirks

Many record-listing endpoints are paginated and return metadata plus the items. Responses commonly look like: {http_code, response_code, response_msg, data: {total, per_page, current_page, last_page, next_page_url, prev_page_url, from, to, data: [ ... ]}} — the array of records is under data.data. Some endpoints return arrays directly under data (e.g., lists of simple items) or under data.messages for send responses; always inspect the endpoint’s response shape and use the appropriate selector (data, data.data, or data.messages).

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