Clinked Python API Docs | dltHub

Clinked is a secure client portal and collaboration platform providing file sharing, version control, groups/spaces, tasks, and integrations via a REST API. The REST API base URL is https://api.clinked.com and all requests require a Bearer access token (OAuth2).

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 Clinked data in under 10 minutes.

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What data can I load from Clinked?

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

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How do I authenticate with the Clinked API?

Clinked uses OAuth2. Obtain an access token via the /oauth/token endpoint using either password grant (username/password) or client_credentials (client_id and client_secret). Use the returned Bearer access_token in the Authorization header: Authorization: Bearer <access_token>.

1. Get your credentials

1. Create/register an application via POST /v2/applications using a temporary user access token (obtained with password grant) to receive client_id and client_secret. 2) Exchange client_id and client_secret for an application access token by calling GET /oauth/token with grant_type=client_credentials (params: client_id, client_secret, scope='read write'). 3) Store the returned access_token and use it in Authorization: Bearer <access_token> for API calls. (Alternatively obtain token via password grant by calling /oauth/token with grant_type=password, client_id=clinked-mobile, username and password.)

2. Add them to .dlt/secrets.toml

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[sources.clinked_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
# OR for direct password grant (not recommended): username = "your_username"
password = "your_password"

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 Clinked 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 clinked_pipeline.py

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

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Pipeline clinked_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset clinked_data
The duckdb destination used duckdb:/clinked.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 clinked_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 userinfo and files_search from the Clinked 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 clinked_source(client_id, client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.clinked.com",
            "auth": {
                "type": "bearer",
                "token": client_id, client_secret,
            },
        },
        "resources": [
            {"name": "userinfo", "endpoint": {"path": "v3/userinfo"}},
            {"name": "files_search", "endpoint": {"path": "v3/groups/{group_id}/files"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="clinked_pipeline",
        destination="duckdb",
        dataset_name="clinked_data",
    )
    load_info = pipeline.run(clinked_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("clinked_pipeline").dataset()
sessions_df = data.userinfo.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM clinked_data.userinfo LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("clinked_pipeline").dataset()
data.userinfo.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 Clinked 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 401 Unauthorized or missing/invalid token errors: ensure you have exchanged credentials at /oauth/token and include Authorization: Bearer <access_token> header. Password grant uses client_id=clinked-mobile; client_credentials requires a registered application's client_id and client_secret.

Rate limits and scope

Documentation notes API rate limits; calls may be throttled. Use application credentials with scope 'read write' when requesting tokens. If tokens expire, re-request /oauth/token using client_credentials or refresh_token flow if provided.

Pagination and list endpoints

Many list endpoints return arrays directly (top-level JSON arrays) or objects containing arrays (e.g., {"envelopes": [...]}); inspect endpoint responses in docs/examples. For large file listings use query parameters (path, pagination if available) per endpoint documentation.

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