McMaster-Carr Python API Docs | dltHub

McMaster-Carr offers a REST API for product information, requiring login and handling various error codes. The API uses standard REST principles and requires valid credentials for authorization. The main endpoints include login and product details. The REST API base URL is https://api.mcmaster.com/v1 and All requests require a Bearer token obtained via a login request using a client certificate..

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 McMaster-Carr data in under 10 minutes.

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What data can I load from McMaster-Carr?

Here are some of the endpoints you can load from McMaster-Carr:

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How do I authenticate with the McMaster-Carr API?

Send a POST to /login with the client certificate, username and password; receive an AuthToken and use it as Authorization: Bearer <token> for all subsequent requests.

1. Get your credentials

1. Go to https://developer.api.mcmaster.ca/ and sign in with your MacID and password.
2. Complete the short registration process that appears after authentication.
3. After registration, request a client certificate; download the certificate and note the accompanying password.
4. Use the certificate, your username, and password to call the POST https://api.mcmaster.com/v1/login endpoint.
5. Capture the returned AuthToken from the response; this token is used as the Bearer token for all API calls.

2. Add them to .dlt/secrets.toml

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[sources.mcmaster_carr_source]
token = "your_auth_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 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 McMaster-Carr 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 mcmaster_carr_pipeline.py

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

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Pipeline mcmaster_carr_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset mcmaster_carr_data
The duckdb destination used duckdb:/mcmaster_carr.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 mcmaster_carr_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 products and categories from the McMaster-Carr 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 mcmaster_carr_source(auth_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.mcmaster.com/v1",
            "auth": {
                "type": "bearer",
                "token": auth_token,
            },
        },
        "resources": [
            {"name": "products", "endpoint": {"path": "products/*", "data_selector": "items"}},
            {"name": "categories", "endpoint": {"path": "categories", "data_selector": "categories"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mcmaster_carr_pipeline",
        destination="duckdb",
        dataset_name="mcmaster_carr_data",
    )
    load_info = pipeline.run(mcmaster_carr_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("mcmaster_carr_pipeline").dataset()
sessions_df = data.products.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM mcmaster_carr_data.products LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("mcmaster_carr_pipeline").dataset()
data.products.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 McMaster-Carr 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