Pocket Python API Docs | dltHub

Pocket is a service to save, organize and retrieve web articles and videos for later reading. The REST API base URL is https://getpocket.com/v3 and OAuth‑style two‑step flow using a consumer_key and user 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 pip install "dlt[workspace]" 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?

All API calls require the consumer_key and access_token to be sent in the request body (JSON or x‑www‑form‑urlencoded). Include the X‑Accept header for JSON responses and set Content‑Type to include charset, e.g. "application/json; charset=UTF-8".

1. Get your credentials

1. Register your application at https://getpocket.com/developer/apps/new to obtain a consumer_key.
2. POST to https://getpocket.com/v3/oauth/request with consumer_key (and optional redirect_uri) to receive a request token (code).
3. Direct the user to https://getpocket.com/auth/authorize?request_token=CODE&redirect_uri=REDIRECT_URI and have them authorize the app.
4. POST to https://getpocket.com/v3/oauth/authorize with consumer_key and the request token to receive an access_token and username.
5. Store the consumer_key and access_token for use in all subsequent API calls.

2. Add them to .dlt/secrets.toml

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[sources.pocket_source]
access_token = "your_user_access_token"

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 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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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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dlt pipeline pocket_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 items_get and items_add 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": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "items_get", "endpoint": {"path": "v3/get", "data_selector": "list"}},
            {"name": "items_add", "endpoint": {"path": "v3/add"}}
        ],
    }
    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_get.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM pocket_data.items_get LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("pocket_pipeline").dataset()
data.items_get.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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Troubleshooting

Authentication failures

If you receive HTTP 400 or 403 from /v3/oauth/request or /v3/oauth/authorize, inspect the X-Error and X-Error-Code headers. Common codes include 138 (missing consumer key), 140 (missing redirect URL), 152 (invalid consumer key), 182/185 (missing or unused code), and 158 (user rejected the request). Ensure the Content-Type and X-Accept headers are set correctly.

Rate limiting and server errors

Pocket may return 5xx responses (code 199 in the X-Error-Code header) indicating a server issue. Implement exponential backoff retries and monitor the response headers for error details.

Pagination and response shape for /v3/get

The /v3/get response is a JSON object containing a list key whose value is an object mapping item IDs to item objects. Use the list key as the data selector. Pagination is achieved via the count, since, and other query parameters; there is no built‑in page token.

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