Simpletexting Python API Docs | dltHub

SimpleTexting is a REST SMS/MMS messaging platform that provides programmatic access to send messages, manage contacts, keywords, campaigns, and webhooks. The REST API base URL is https://app2.simpletexting.com/v1 and All requests require an API token (Bearer) for authentication; legacy token-in-query support exists for very old v1 users..

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 Simpletexting data in under 10 minutes.

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What data can I load from Simpletexting?

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

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How do I authenticate with the Simpletexting API?

SimpleTexting uses a Bearer token for authentication (API token found in dashboard). Include header: Authorization: Bearer <TOKEN> and Accept: application/json. Legacy token query parameter ?token= is deprecated.

1. Get your credentials

1. Sign up for a SimpleTexting account. 2) Request API access via Account > API settings or by emailing support@simpletexting.net with your use case. 3) Once approved, retrieve your API token at https://app2.simpletexting.com/account/api. 4) Store the token securely.

2. Add them to .dlt/secrets.toml

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[sources.simpletexting_source]
api_token = "your_api_token_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 Simpletexting 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 simpletexting_pipeline.py

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

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Pipeline simpletexting_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset simpletexting_data
The duckdb destination used duckdb:/simpletexting.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 simpletexting_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 messages_sent and contacts from the Simpletexting 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 simpletexting_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app2.simpletexting.com/v1",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "messages_sent", "endpoint": {"path": "messaging/sent/list", "data_selector": "list"}},
            {"name": "contacts", "endpoint": {"path": "group/contact/list", "data_selector": "contacts"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="simpletexting_pipeline",
        destination="duckdb",
        dataset_name="simpletexting_data",
    )
    load_info = pipeline.run(simpletexting_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("simpletexting_pipeline").dataset()
sessions_df = data.messages_sent.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM simpletexting_data.messages_sent LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("simpletexting_pipeline").dataset()
data.messages_sent.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 Simpletexting 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 errors like invalidToken or 401/403 responses, ensure you are sending the API token. Use Authorization: Bearer <API_TOKEN>. Older examples show ?token= query parameter but this is deprecated for new accounts.

Rate limits and spam approval

API access is approval‑only to prevent spam. If you are denied or hit platform limits, contact support@simpletexting.net; the docs note API access must be requested and may be restricted.

Pagination and empty lists

List endpoints (e.g. /messaging/sent/list, /keyword/list, /messaging/scheduled/list) return a JSON object with a list array (or endpoint‑specific arrays like contacts). When empty, arrays are returned as []. Always check the response code and message fields for status and errors.

Common API error responses

The API returns structured responses including fields like code, message, and endpoint‑specific fields. Common error codes/messages in examples: invalidToken, missingRequirement, billingIssue, unknownError, invalidContact, invalidDate, invalidCampaign, creditLimitReached, alreadyAdded.

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