Mopinion Python API Docs | dltHub

Mopinion is a user feedback platform and REST API that provides programmatic access to feedback, datasets, reports, deployments and related metadata. The REST API base URL is https://api.mopinion.com and All requests (except /ping and /token) require an x-auth-token header built from your public key and an HMAC‑SHA256 signature..

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 Mopinion data in under 10 minutes.

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What data can I load from Mopinion?

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

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How do I authenticate with the Mopinion API?

Authentication uses a Base64‑encoded token placed in the x-auth-token header. The token is formed as BASE64(PUBLIC_KEY: HMAC.SHA256(RESOURCE_URI|JSON_BODY)) where the HMAC is computed with your signature token.

1. Get your credentials

1. Log in to Mopinion Suite. 2) Navigate to Settings » Feedback API (classic) or Integrations » Feedback API (Raspberry). 3) Create a new credential set and record the public_key, private_key, and signature token. 4) Keep the private_key and signature token secret. Alternatively, call GET /token with Basic auth (public_key:private_key) to retrieve the signature token.

2. Add them to .dlt/secrets.toml

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[sources.mopinion_source]
public_key = "your_public_key_here"
private_key = "your_private_key_here"
signature_token = "your_signature_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 Mopinion 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 mopinion_pipeline.py

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

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Pipeline mopinion_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset mopinion_data
The duckdb destination used duckdb:/mopinion.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 mopinion_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 datasets and datasets_feedback from the Mopinion 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 mopinion_source(public_key, private_key, signature_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.mopinion.com",
            "auth": {
                "type": "http_basic",
                "x-auth-token": public_key, private_key, signature_token,
            },
        },
        "resources": [
            {"name": "datasets", "endpoint": {"path": "datasets"}},
            {"name": "datasets_feedback", "endpoint": {"path": "datasets/{dataset_id}/feedback"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mopinion_pipeline",
        destination="duckdb",
        dataset_name="mopinion_data",
    )
    load_info = pipeline.run(mopinion_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("mopinion_pipeline").dataset()
sessions_df = data.datasets.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM mopinion_data.datasets LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("mopinion_pipeline").dataset()
data.datasets.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 Mopinion 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 failures

If x-auth-token is missing or invalid the API returns a 401‑style problem JSON (example: {"status":401,"error_code":18,"title":"The credentials you provided are not valid","type":"https://developer.mopinion.com/api/error-codes"}). Ensure you compute HMAC.SHA256(RESOURCE_URI|JSON_BODY) with the signature token and encode BASE64(PUBLIC_KEY:HEX_HMAC).

Obtaining signature token (/token)

Use GET /token with Basic auth header (public_key:private_key) to retrieve JSON {"token":"..."}. This token is used to sign requests or can be retrieved from Mopinion Suite settings.

Pagination and metadata

Collection endpoints are paginated (limit, page). Use the _meta property (has_more, next, previous, count, total) to iterate pages. Default limit is 10. Use _meta.next or page and limit query params for further pages.

Verbosity / HATEOAS

By default responses include _meta and resource(s) at top‑level. If you need HATEOAS links and a data wrapper, add header verbosity: full – full responses will wrap resources in data and include _links.

Common API errors

401 authentication errors (invalid credentials/key/signature), 404 resource not found, 400 bad request (validation). Error responses follow application/problem+json with properties: status, error_code, title, type, details.

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