Meta Box Python API Docs | dltHub

The MB REST API extension allows custom fields to be accessed via WordPress REST API. It retrieves and updates custom fields in REST API responses. Install Meta Box and MB REST API plugins to use it. The REST API base URL is https://{your_site}/wp-json/ and Uses WordPress REST API authentication; GETs for public content usually require no auth; modifying endpoints require WordPress authentication (HTTP Basic, cookie, OAuth/JWT depending on WP setup)..

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 Meta Box data in under 10 minutes.

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What data can I load from Meta Box?

Here are some of the endpoints you can load from Meta Box:

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How do I authenticate with the Meta Box API?

MB REST API leverages WordPress authentication. For POST (update) requests you must authenticate with a WordPress account using HTTP Basic (username:password), cookie‑based login, or any WordPress‑supported REST auth plugin (e.g., JWT, OAuth). Include the appropriate Authorization header or cookies.

1. Get your credentials

1. Log into the WordPress admin dashboard.
2. Navigate to Users → All Users and ensure you have an account with the appropriate role (e.g., Administrator or Editor).
3. For basic auth, note the username and password of that account.
4. (Optional) If using a JWT plugin, install the plugin, then follow its instructions to generate a token (usually via a POST to /wp-json/jwt-auth/v1/token with the same username/password).
5. For cookie‑based auth, authenticate via a browser session and pass the WP‑nonce and cookies with each request.

2. Add them to .dlt/secrets.toml

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[sources.meta_box_rest_api_source]
username = "your_wp_username"
password = "your_wp_password"
# or for JWT
jwt_token = "your_jwt_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 Meta Box 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 meta_box_rest_api_pipeline.py

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

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Pipeline meta_box_rest_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset meta_box_rest_api_data
The duckdb destination used duckdb:/meta_box_rest_api.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 meta_box_rest_api_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 post and posts from the Meta Box 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 meta_box_rest_api_source(username=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{your_site}/wp-json/",
            "auth": {
                "type": "http_basic",
                "password": username,
            },
        },
        "resources": [
            {"name": "posts", "endpoint": {"path": "wp/v2/posts"}},
            {"name": "post", "endpoint": {"path": "wp/v2/{post_type}/{id}", "data_selector": "meta_box"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="meta_box_rest_api_pipeline",
        destination="duckdb",
        dataset_name="meta_box_rest_api_data",
    )
    load_info = pipeline.run(meta_box_rest_api_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("meta_box_rest_api_pipeline").dataset()
sessions_df = data.posts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM meta_box_rest_api_data.posts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("meta_box_rest_api_pipeline").dataset()
data.posts.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 Meta Box 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 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