Mirantis Kubernetes Engine Python API Docs | dltHub

MKE API is a REST API that provides programmatic access to Mirantis Kubernetes Engine management functions. The REST API base URL is https://<UCP_HOSTNAME>/api/ucp and All requests require a Bearer token passed in the Authorization header..

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 Mirantis Kubernetes Engine data in under 10 minutes.

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What data can I load from Mirantis Kubernetes Engine?

Here are some of the endpoints you can load from Mirantis Kubernetes Engine:

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How do I authenticate with the Mirantis Kubernetes Engine API?

Use the HTTP header Authorization: Bearer <token> for all API calls.

1. Get your credentials

1. Log in to the Mirantis Kubernetes Engine (MKE) admin console.
2. Navigate to Access > API Tokens (or similar).
3. Click Create Token, give it a name, and copy the generated token.
4. For SCIM access, open the SCIM configuration section in the console, locate the SCIM Token field, and either enter an existing token or click Generate to create a new one.
5. Store the token securely; it will be used as the Bearer token in API requests.

2. Add them to .dlt/secrets.toml

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[sources.mirantis_kubernetes_engine_source]
api_token = "your_bearer_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 Mirantis Kubernetes Engine 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 mirantis_kubernetes_engine_pipeline.py

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

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Pipeline mirantis_kubernetes_engine_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset mirantis_kubernetes_engine_data
The duckdb destination used duckdb:/mirantis_kubernetes_engine.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 mirantis_kubernetes_engine_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 backups and users from the Mirantis Kubernetes Engine 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 mirantis_kubernetes_engine_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<UCP_HOSTNAME>/api/ucp",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "backups", "endpoint": {"path": "api/ucp/backups"}},
            {"name": "users", "endpoint": {"path": "enzi/v0/scim/v2/Users", "data_selector": "Resources"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mirantis_kubernetes_engine_pipeline",
        destination="duckdb",
        dataset_name="mirantis_kubernetes_engine_data",
    )
    load_info = pipeline.run(mirantis_kubernetes_engine_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("mirantis_kubernetes_engine_pipeline").dataset()
sessions_df = data.backups.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM mirantis_kubernetes_engine_data.backups LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("mirantis_kubernetes_engine_pipeline").dataset()
data.backups.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 Mirantis Kubernetes Engine 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

• 401 Unauthorized – The Bearer token is missing, expired, or invalid. Ensure you are sending Authorization: Bearer <token> with a current token from the MKE UI.

Permission errors

• 403 Forbidden – The token does not have sufficient privileges for the requested endpoint. Use an admin token or grant the necessary scopes.

Resource errors

• 404 Not Found – The requested backup or SCIM object ID does not exist. Verify the identifier.
• 400 Bad Request – The request format is invalid (e.g., malformed query parameters). Check the endpoint documentation for required parameters.

Server errors

• 500 Internal Server Error – An unexpected error occurred on the MKE side. Retry the request; if the problem persists, contact Mirantis support.

Pagination (SCIM endpoints)

• SCIM resources support startIndex and count query parameters. Responses include totalResults, itemsPerPage, startIndex, and the Resources array. Adjust startIndex to page through large result sets.

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