Load MyFitnessPal data to DuckDB

MyFitnessPal provides a private, partner-only REST API for integrating health and fitness data with their platform service via OAuth 2.0 authentication. Everything needed to build a working MyFitnessPal → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your MyFitnessPal to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from MyFitnessPal to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the MyFitnessPal API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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MyFitnessPal API at a glance

These values come from the MyFitnessPal API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the MyFitnessPal API?

The MyFitnessPal API uses OAuth 2.0 authorization code flow to obtain access tokens. Requests to the API must include this access token, typically via an Authorization header.

1. Get your credentials

The MyFitnessPal API is not publicly accessible via a self-service dashboard. You must apply for access by emailing api@myfitnesspal.com with details about your company and use case. If approved for the Partner Program, you will receive OAuth 2.0 client credentials (Client ID and Client Secret) directly from their team. Once you have these, follow the OAuth 2.0 authorization code flow to obtain access tokens for individual users.

2. Add them to .dlt/secrets.toml

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[sources.myfitnesspal_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
redirect_uri = "https://your-app.com/callback"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What MyFitnessPal data can I load into DuckDB?

These are the MyFitnessPal endpoints dlt can load into DuckDB:

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How do I load only new MyFitnessPal records?

The MyFitnessPal API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "diary", "endpoint": {
    "path": "diary",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated MyFitnessPal pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /authorization and /token from the MyFitnessPal API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def myfitnesspal_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.myfitnesspal.com/v2",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "diary", "endpoint": {"path": "diary", "data_selector": "items"}},
            {"name": "water", "endpoint": {"path": "diary/water", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def load_myfitnesspal_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="myfitnesspal_pipeline",
        destination="duckdb",
        dataset_name="myfitnesspal_data",
    )
    load_info = pipeline.run(myfitnesspal_source())
    print(load_info)


if __name__ == "__main__":
    load_myfitnesspal_to_duckdb()

Run it with python myfitnesspal_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query MyFitnessPal data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("myfitnesspal_pipeline").dataset()
df = data.diary.df()
print(df.head())

SQL:

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SELECT * FROM myfitnesspal_data.diary LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the MyFitnessPal to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw MyFitnessPal loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load MyFitnessPal data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack