Checking Product Schema On a Raspberry Pi

Goal of the mini-project

The aim here is to –

Verify that product schema (JSON-LD) is implemented correctly on example.co.uk after the migration to Adobe Commerce (Magento).
The script crawls your chosen product URLs and reports if required fields like price, brand, sku, and availability are present.

Step 1 – Open a terminal

Click the black terminal icon on the Pi desktop.

Step 2 – Check Python 3

python3 --version

You should see something like Python 3.9.2 (any 3.7+ is fine).

Step 3 – Install libraries

sudo apt update
pip3 install requests beautifulsoup4

Step 4 – Create a working folder

mkdir ~/schema_check
cd ~/schema_check

Step 5 – Create the script file

nano check_schema.py

Then paste this entire script:

import requests, json, csv, time
from bs4 import BeautifulSoup

# ---------- configuration ----------
# Put your product URLs here (you can add as many as you like)
urls = [
    "https://www.example.co.uk/example-product-1",
    "https://www.example.co.uk/example-product-2"
]

# Fields you want to confirm exist in the Product schema
required_fields = ["name", "brand", "sku", "price", "priceCurrency", "availability"]

# Optional delay between requests (seconds)
delay = 2

# ---------- functions ----------
def extract_product_schema(url):
    try:
        r = requests.get(url, timeout=15)
        soup = BeautifulSoup(r.text, "html.parser")
        for tag in soup.find_all("script", type="application/ld+json"):
            try:
                data = json.loads(tag.string)
                if isinstance(data, list):
                    for item in data:
                        if item.get("@type") == "Product":
                            return item
                elif data.get("@type") == "Product":
                    return data
            except Exception:
                continue
    except Exception as e:
        print(f"Error fetching {url}: {e}")
    return None

def check_fields(product_json):
    found = json.dumps(product_json)
    return [f for f in required_fields if f not in found]

# ---------- main ----------
results = []
for u in urls:
    print(f"Checking {u} ...")
    product = extract_product_schema(u)
    if not product:
        print(f"❌ No Product schema found: {u}")
        results.append([u, "No Product schema", ""])
    else:
        missing = check_fields(product)
        if missing:
            print(f"⚠ Missing: {', '.join(missing)}")
            results.append([u, "Missing fields", ", ".join(missing)])
        else:
            print(f"✅ All key fields present")
            results.append([u, "All fields present", ""])
    time.sleep(delay)

# ---------- save to CSV ----------
with open("schema_results.csv", "w", newline="") as f:
    writer = csv.writer(f)
    writer.writerow(["URL", "Status", "Missing Fields"])
    writer.writerows(results)

print("\nDone! Results saved to schema_results.csv")

Save and exit:

Ctrl + O, Enter → save
Ctrl + X → exit

Step 6 – Edit your URLs

Later, open the script again (nano check_schema.py) and replace the two example links with your 10–50 product URLs.
Each URL must be inside quotes and separated by commas.

Step 7 – Run the script

python3 check_schema.py

It will:

Fetch each page
Extract the Product JSON-LD
Report any missing fields
Save a summary to schema_results.csv in the same folder

Step 8 – View the results

cat schema_results.csv

or open the file in LibreOffice Calc / Excel.

Example output:

URL,Status,Missing Fields
https://www.example.co.uk/football-goal.html,All fields present,
https://www.example.co.uk/tennis-net.html,Missing fields,priceCurrency availability
https://www.example.co.uk/baseball-bat.html,No Product schema,

Optional tweaks

Increase delay = 2 to 5 if you test hundreds of URLs (avoids rate limits).
You can import hundreds of URLs from a CSV by editing the script — ask later if you’d like that version.
Re-run anytime to confirm schema fixes.

Quick recap

StepActionCommand

1Open terminal(click icon)

2Check Pythonpython3 --version

3Install depspip3 install requests beautifulsoup4

4Make foldermkdir ~/schema_check & cd ~/schema_check

5Create scriptnano check_schema.py

6Edit URLsinside script

7Run itpython3 check_schema.py

8View resultscat schema_results.csv

That’s it. Job done.

You’ve now got a simple tool that checks your product schema in seconds. No fancy platforms. No monthly fees. Just a Raspberry Pi doing proper work.

Run it whenever you push changes. Catch broken schema before Google does. Keep your rich results intact.

The script sits there, ready to go. Update your URLs. Hit run. Get answers.

This is what proper validation looks like – fast, local, and under your control.