Midigator Python API Docs | dltHub

Midigator is an API that provides services for fraud prevention, chargeback management, and order validation. The REST API base URL is https://api.midigator.com and All requests require a short-lived JWT bearer token obtained by exchanging a long-lived API Secret..

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 Midigator data in under 10 minutes.

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What data can I load from Midigator?

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

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How do I authenticate with the Midigator API?

Authentication involves sending a long-lived API Secret to the /auth endpoint to receive a short-lived JWT bearer token. This bearer token must then be included in the Authorization: Bearer TOKEN header for all subsequent API calls.

1. Get your credentials

To obtain your API Secret, contact your dedicated Customer Success Manager (CSM) or email support@midigator.com.

2. Add them to .dlt/secrets.toml

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[sources.midigator_source]
api_secret = "your_api_secret_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 Midigator 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 midigator_pipeline.py

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

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Pipeline midigator_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset midigator_data
The duckdb destination used duckdb:/midigator.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 midigator_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 subscribe and order from the Midigator 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 midigator_source(api_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.midigator.com",
            "auth": {
                "type": "bearer",
                "token": api_secret,
            },
        },
        "resources": [
            {"name": "subscribe", "endpoint": {"path": "subscribe"}},
            {"name": "order", "endpoint": {"path": "order/{order_guid}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="midigator_pipeline",
        destination="duckdb",
        dataset_name="midigator_data",
    )
    load_info = pipeline.run(midigator_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("midigator_pipeline").dataset()
sessions_df = data.subscribe.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM midigator_data.subscribe LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("midigator_pipeline").dataset()
data.subscribe.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 Midigator 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

API Error Responses

The Midigator API returns standard HTTP status codes to indicate the success or failure of an API request. Common error responses include:

• 400 Bad Request: Indicates that the request was malformed or invalid. The response body will typically contain a JSON object with {"error":true,"message":"..."} providing more details.
• 401 Unauthorized: Occurs when authentication fails, either due to a missing or invalid API Secret during token generation, or an expired or invalid bearer token for subsequent requests. The response body will also contain {"error":true,"message":"Unauthorized"}.
• 404 Not Found: Returned when the requested resource does not exist, for example, if a chargeback_guid or event_guid does not correspond to an existing entity.
• 500 Internal Server Error: A generic error indicating an issue on the Midigator API server. These errors should be reported to Midigator support.

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