Katana MRP Python API Docs | dltHub

Katana MRP is a manufacturing resource planning platform that offers a REST API for managing products, inventory, orders, and other manufacturing data. The REST API base URL is https://api.katanamrp.com/v1 and All requests require a Bearer token for authentication..

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 Katana MRP data in under 10 minutes.

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What data can I load from Katana MRP?

Here are some of the endpoints you can load from Katana MRP:

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How do I authenticate with the Katana MRP API?

Authentication is performed by sending the API key as a Bearer token in the Authorization header of each request.

1. Get your credentials

1. Log in to your Katana account.
2. Navigate to Settings → API Keys.
3. Click Create New API Key.
4. Give the key a name and optionally set scopes.
5. Save the key and copy the generated value.
6. Store the key securely; it will be used as a Bearer token in API calls.

2. Add them to .dlt/secrets.toml

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[sources.katana_mrp_source]
api_key = "your_api_key_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 Katana MRP 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 katana_mrp_pipeline.py

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

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Pipeline katana_mrp_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset katana_mrp_data
The duckdb destination used duckdb:/katana_mrp.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 katana_mrp_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 sales_orders from the Katana MRP 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 katana_mrp_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.katanamrp.com/v1",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "products", "endpoint": {"path": "products", "data_selector": "data"}},
            {"name": "sales_orders", "endpoint": {"path": "sales_orders", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM katana_mrp_data.products LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("katana_mrp_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 Katana MRP 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 Errors

• 401 Unauthorized – Returned when the Authorization header is missing, malformed, or contains an invalid API key. Verify that you are sending Authorization: Bearer <your_api_key>.

Rate Limiting

• 429 Too Many Requests – Katana enforces rate limits per account. If you receive this response, implement exponential back‑off and respect the Retry-After header if present.

Pagination

• List endpoints return paginated results. Use the page and page_size query parameters (default page size is 50). Continue fetching pages until the response contains an empty data array.

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