Nice Expert Help Python API Docs | dltHub

Nice Expert Help is the CXone Mpower Expert REST API for programmatic access to site content, users, analytics, and integrations. The REST API base URL is https://{hostname}/@api/deki and all requests require an API token supplied via the X-Deki-Token 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 add "dlt[hub]" and start loading Nice Expert Help data in under 10 minutes.

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What data can I load from Nice Expert Help?

Here are some of the endpoints you can load from Nice Expert Help:

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How do I authenticate with the Nice Expert Help API?

All API requests require an API token supplied via the 'X-Deki-Token' header.

1. Get your credentials

1. Log in to your CXone instance and navigate to the Admin application. 2. Select the specific account or user for which you need to create an integration. 3. Navigate to the Access Keys tab. 4. Click 'Generate New Access Key'. 5. Copy the 'Access Key ID' and the 'Secret Access Key'. Note: The secret key is shown only once; ensure you save it securely immediately, as you will need to create a new key if it is lost. 6. If your integration requires OAuth2 application registration, ensure your application is registered via the DEVone Developer Community to obtain your 'Client ID' and 'Client Secret'.

2. Add them to .dlt/secrets.toml

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[sources.nice_expert_help_source]
deki_token = "REPLACE_ME"

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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 Nice Expert Help 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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uv run python nice_expert_help_pipeline.py

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

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Pipeline nice_expert_help_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset nice_expert_help_data
The duckdb destination used duckdb:/nice_expert_help.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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uv run dlthub 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 /auth/authorize and /auth/token from the Nice Expert Help 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 nice_expert_help_source(deki_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{hostname}/@api/deki",
            "auth": {"type": "api_key", "api_key": deki_token, "name": "X-Deki-Token", "location": "header"},
        },
        "resources": [
            {"name": "site_query", "endpoint": {"path": "/@api/deki/site/query"}},
            {"name": "site_jobs", "endpoint": {"path": "/@api/deki/site/jobs"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="nice_expert_help_pipeline",
        destination="duckdb",
        dataset_name="nice_expert_help_data",
    )
    load_info = pipeline.run(nice_expert_help_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("nice_expert_help_pipeline").dataset()
sessions_df = data.site_query.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM nice_expert_help_data.site_query LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("nice_expert_help_pipeline").dataset()
data.site_query.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 Nice Expert Help 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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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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