Pocket Python API Docs | dltHub

Pocket is a service that allows users to save articles, videos, and pages for later reading, providing an API for developers to programmatically interact with user lists and account data. The REST API base URL is https://getpocket.com/v3 and uses a proprietary OAuth-variant authentication flow involving a consumer key and access token.

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 Pocket data in under 10 minutes.

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What data can I load from Pocket?

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

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How do I authenticate with the Pocket API?

Authentication uses a consumer key and an access token passed in the request body for POST requests. The API requires the header 'X-Accept: application/json' and 'Content-Type: application/json'.

1. Get your credentials

1. Go to the Pocket Developer Portal at https://getpocket.com/developer/apps/new. 2. Sign in to your Pocket account. 3. Click "Create New App". 4. Provide your Application Name, Description, and select the required Permissions (e.g., "Retrieve", "Add", or "Modify"). 5. Select "Desktop (other)" as the platform. 6. Click "Create Application" to receive your Consumer Key. 7. To obtain an Access Token, perform the OAuth authentication flow: POST to /v3/oauth/request with your Consumer Key, redirect your user to the authorize URL, and then POST to /v3/oauth/authorize to exchange the authorized code for your permanent Access Token.

2. Add them to .dlt/secrets.toml

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[sources.pocket_source]
consumer_key = "your_consumer_key_here"
access_token = "your_access_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 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 Pocket 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 pocket_pipeline.py

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

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Pipeline pocket_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset pocket_data
The duckdb destination used duckdb:/pocket.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 /v3/get and /v3/send from the Pocket 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 pocket_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://getpocket.com/v3",
            "auth": {"type": "api_key", "api_key": access_token, "name": "access_token"},
        },
        "resources": [
            {"name": "items", "endpoint": {"path": "v3/get", "data_selector": "list"}},
            {"name": "modify_list", "endpoint": {"path": "v3/send"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pocket_pipeline",
        destination="duckdb",
        dataset_name="pocket_data",
    )
    load_info = pipeline.run(pocket_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("pocket_pipeline").dataset()
sessions_df = data.items.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM pocket_data.items LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("pocket_pipeline").dataset()
data.items.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 Pocket 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