MyDataScope Python API Docs | dltHub

Build a MyDataScope-to-database pipeline in Python using dlt with AI Workbench support for Claude Code, Cursor, and Codex.

Last updated: Mar 10, 2026

MyDataScope is a REST API for accessing DataScope (MyDataScope) platform data such as form answers, locations, lists (metadata), notifications and generated files. The REST API base URL is https://www.mydatascope.com/api/external and All requests require an Authorization header set to your DataScope 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 MyDataScope data in under 10 minutes.

What data can I load from MyDataScope?

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

How do I authenticate with the MyDataScope API?

API key-based authentication. Include your API key in the Authorization HTTP header exactly as the key value (no Bearer prefix).

1. Get your credentials

Sign in to your MyDataScope (DataScope) account. 2) Open developer/webhooks or API keys section (https://www.mydatascope.com/webhooks). 3) Create/register a new API key. 4) Copy the key and store it securely; use it as the value of the Authorization header for API requests.

2. Add them to .dlt/secrets.toml


[sources.my_data_scope_source]
api_key = "your_datascope_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.

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:


uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:


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:


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 MyDataScope 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:


python my_data_scope_pipeline.py

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


Pipeline my_data_scope_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset my_data_scope_data
The duckdb destination used duckdb:/my_data_scope.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:


dlt pipeline my_data_scope_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.

Python pipeline example

This example loads answers and locations from the MyDataScope API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:


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

@dlt.source
def my_data_scope_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.mydatascope.com/api/external",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "answers", "endpoint": {"path": "v2/answers"}},
            {"name": "locations", "endpoint": {"path": "locations"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="my_data_scope_pipeline",
        destination="duckdb",
        dataset_name="my_data_scope_data",
    )
    load_info = pipeline.run(my_data_scope_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.

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):


import dlt

data = dlt.pipeline("my_data_scope_pipeline").dataset()
sessions_df = data.answers.df()
print(sessions_df.head())

SQL (DuckDB example):


SELECT * FROM my_data_scope_data.answers LIMIT 10;

In a marimo or Jupyter notebook:


import dlt

data = dlt.pipeline("my_data_scope_pipeline").dataset()
data.answers.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

What destinations can I load MyDataScope data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Destination	Example value
DuckDB (local, default)	`"duckdb"`
PostgreSQL	`"postgres"`
BigQuery	`"bigquery"`
Snowflake	`"snowflake"`
Redshift	`"redshift"`
Databricks	`"databricks"`
Filesystem (S3, GCS, Azure)	`"filesystem"`

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

Troubleshooting

Authentication failures

If you receive 401 Unauthorized, verify the Authorization header contains your API key exactly (no "Bearer " prefix). Ensure the key is active in the DataScope developer/webhooks dashboard.

Rate limits and pagination

Endpoints return up to a maximum 'limit' (default and max noted on docs: answers default 200, max 200; metadata/answers variant may use higher limits). Use offset (pagination) to retrieve subsequent pages. If you hit 429 Too Many Requests, back off and retry later.

Common HTTP errors

400 Bad Request: malformed parameters. Check query parameter names (form_id, user_id, start, end, limit, offset, metadata_type, metadata_id).
401 Unauthorized: invalid/absent API key.
403 Forbidden: resource restricted.
404 Not Found: wrong endpoint or missing resource.
429 Too Many Requests: rate-limiting — slow requests and paginate.
500/503: server-side issues — retry with exponential backoff.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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.


dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

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