Cincopa Python API Docs | dltHub

Cincopa is a multimedia management platform exposing a REST API to manage galleries, assets, tokens, and related resources. The REST API base URL is https://api.cincopa.com/v2/ and all requests require an api_token query parameter for authentication (temporary tokens also supported).

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 Cincopa data in under 10 minutes.

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What data can I load from Cincopa?

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

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How do I authenticate with the Cincopa API?

Authentication is performed via an api_token supplied as a query parameter (api_token) on requests. The platform also supports issuing temporary tokens via token.get_temp.json for limited-scope client use.

1. Get your credentials

1. Log into your Cincopa account. 2) Go to the API / developer area (Account > API or Developer settings). 3) Create a new API app/token and set desired permissions. 4) Copy the generated api_token and use it in requests. (Docs: https://developer.cincopa.com/apis, https://www.cincopa.com/help/how-to-get-an-api-key/)

2. Add them to .dlt/secrets.toml

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[sources.cincopa_source]
api_token = "your_api_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 Cincopa 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 cincopa_pipeline.py

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

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Pipeline cincopa_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cincopa_data
The duckdb destination used duckdb:/cincopa.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 cincopa_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 asset.list and gallery.list from the Cincopa 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 cincopa_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.cincopa.com/v2/",
            "auth": {
                "type": "api_key",
                "api_token": api_token,
            },
        },
        "resources": [
            {"name": "asset_list", "endpoint": {"path": "asset.list.json", "data_selector": "items"}},
            {"name": "gallery_list", "endpoint": {"path": "gallery.list.json", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cincopa_pipeline",
        destination="duckdb",
        dataset_name="cincopa_data",
    )
    load_info = pipeline.run(cincopa_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("cincopa_pipeline").dataset()
sessions_df = data.asset_list.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cincopa_data.asset_list LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cincopa_pipeline").dataset()
data.asset_list.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 Cincopa 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 you receive 403 Unauthorized, verify your api_token is valid and passed as api_token either as query parameter or in request body where required. Use /ping.json to validate credentials.

Temporary tokens and permissions

Temporary tokens from /token.get_temp.json are scoped by permissions and TTL; requests using expired or insufficient-scope tokens will fail.

Pagination and large responses

List endpoints (asset.list, gallery.list, gallery.get_items) return 'items' array and an 'items_data' object with page, items_per_page, items_count and pages_count. Use paging parameters to iterate pages to avoid large responses.

Common errors

403  invalid or missing api_token; 500  server error; 400  bad request or missing required parameters. Use error HTTP code plus response 'success' boolean to detect failures.

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