How to Resolve Amazon MWS Order Acknowledgement Error 25: Invalid Items in XML Feed

As a data scientist or software engineer working with Amazon Marketplace Web Service (MWS), you might have encountered Error 25. This error arises when attempting to acknowledge orders but the XML feed contains one or more invalid items. In this post, addressing a technical audience, we will explore how to handle this common issue.

How to Resolve Amazon MWS Order Acknowledgement Error 25: Invalid Items in XML Feed

As a data scientist or software engineer working with Amazon Marketplace Web Service (MWS), you might have encountered Error 25. This error arises when attempting to acknowledge orders but the XML feed contains one or more invalid items. In this post, addressing a technical audience, we will explore how to handle this common issue.

Understanding Error 25

Before delving into the solution, let’s understand what Error 25 is. Amazon MWS uses XML feeds to exchange data between your application and the Amazon marketplace. Error 25 is returned when Amazon MWS is unable to process your XML feed due to invalid items. This could be caused by several issues, such as incorrect formatting, invalid SKU identifiers, or data that doesn’t match Amazon’s records.

Step 1: Validate Your XML

Amazon MWS provides an XSD (XML Schema Definition) for each feed type. These XSD files contain rules for how your XML document should be structured. Your first step should be validating your XML data against the appropriate XSD.

import lxml.etree as ET

def validate_xml(xml_path, xsd_path):
    xmlschema_doc = ET.parse(xsd_path)
    xmlschema = ET.XMLSchema(xmlschema_doc)
    
    xml_doc = ET.parse(xml_path)
    
    result = xmlschema.validate(xml_doc)
    return result

This Python function will return True if your XML is valid, or False if it’s not. If your XML is invalid, you’ll need to correct it before proceeding.

Step 2: Check Your Data

Even if your XML is structurally correct, it can still contain invalid data. For instance, you might be using SKU identifiers that aren’t recognized by Amazon, or your quantity values might be incorrect.

To check your data, you can manually review it, or use a script to compare the SKUs in your feed against your product list. Make sure your quantity values are positive integers, and that your SKU identifiers match exactly with those in your Amazon seller account.

Step 3: Test Your Feed

Amazon MWS includes a sandbox environment for testing feeds. Before submitting your feed to the live Amazon MWS, consider uploading it to the sandbox first. This will allow you to catch any errors before they affect your live data.

Step 4: Handle Error 25

If you’ve done all the above and are still receiving Error 25, you’ll need to handle it in your code. Here’s how you might do that in Python:

import mws

def handle_error_25():
    try:
        # Your code to acknowledge orders goes here.
    except mws.MWSError as e:
        if e.code == 25:
            # Your code to handle the error goes here.
            print(f"Error 25 encountered: {e.detail}")

In this example, we’re using the mws Python package to interact with Amazon MWS. If an error is raised during order acknowledgement, we check if it’s Error 25 and handle it accordingly.

Conclusion

Error 25 can be a common issue when working with Amazon MWS, but with careful validation, data checking, and error handling, it can be resolved. By following these steps, you should be able to avoid or correct this error, ensuring smooth interaction with the Amazon MWS API.

Keywords

Amazon MWS, Error 25, XML feed, invalid items, data validation, SKU identifiers, Python, mws package, sandbox testing, order acknowledgement, XML Schema Definition, XSD.

Keep in mind that understanding and resolving technical errors like this one are part of the routine when dealing with complex APIs. Don’t be discouraged, keep learning, keep coding, and remember: every problem has a solution.


About Saturn Cloud

Saturn Cloud is your all-in-one solution for data science & ML development, deployment, and data pipelines in the cloud. Spin up a notebook with 4TB of RAM, add a GPU, connect to a distributed cluster of workers, and more. Join today and get 150 hours of free compute per month.