MongoDB Python API Docs | dltHub

MongoDB REST API is a collection of APIs that provide programmatic access to MongoDB databases and services, including Atlas Administration, Atlas Data, and Relational Migrator, as well as third-party implementations. The REST API base URL is https://cloud.mongodb.com/api/atlas/v1.0 and Authentication for MongoDB REST APIs typically involves API keys or service account access tokens (OAuth 2.0)..

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 MongoDB data in under 10 minutes.

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What data can I load from MongoDB?

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

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How do I authenticate with the MongoDB API?

For the Atlas Administration API, authentication can be done using API keys with HTTP Digest Authentication or service account access tokens (OAuth 2.0). For other implementations like restdb.io, an API key is typically passed via the x-apikey header, and for some open-source implementations, a token might be sent in an X-TOKEN header.

1. Get your credentials

To obtain API keys for the MongoDB Atlas Administration API, you would typically log into your MongoDB Atlas account, navigate to the 'Access Manager' or 'API Keys' section, and generate new API keys. For service account access tokens (OAuth 2.0), you would follow the OAuth 2.0 flow to obtain the token. For restdb.io, API keys are available in your restdb.io dashboard.

2. Add them to .dlt/secrets.toml

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[sources.mongodb_instance_source]
api_key = "your_api_key_here"
token = "your_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 MongoDB 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 mongodb_instance_pipeline.py

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

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Pipeline mongodb_instance_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset mongodb_instance_data
The duckdb destination used duckdb:/mongodb_instance.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 mongodb_instance_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 groups and customers from the MongoDB 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 mongodb_instance_source(api_key, token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://cloud.mongodb.com/api/atlas/v1.0",
            "auth": {
                "type": "api_key, bearer",
                "api_key, token": api_key, token,
            },
        },
        "resources": [
            {"name": "groups", "endpoint": {"path": "groups"}},
            {"name": "customers", "endpoint": {"path": "rest/customers"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mongodb_instance_pipeline",
        destination="duckdb",
        dataset_name="mongodb_instance_data",
    )
    load_info = pipeline.run(mongodb_instance_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("mongodb_instance_pipeline").dataset()
sessions_df = data.groups.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM mongodb_instance_data.groups LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("mongodb_instance_pipeline").dataset()
data.groups.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 MongoDB 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

If you encounter a 401 error when using the Atlas Administration API, it indicates that the service account or API keys used for authentication are not members of the organization. Ensure your credentials have the necessary permissions.

Pagination and Record Limits

When querying APIs like restdb.io, be aware of default record limits. For instance, restdb.io returns a maximum of 1000 records per query by default. To retrieve more records, you may need to use pagination parameters such as $max to specify the desired limit. The response might also include a totals object with pagination metadata like total, count, skip, and max.

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