Free Product Feed Analyzer for UCP & ACP
Analyze your product data quality for AI agent visibility. Check Schema.org Product markup for UCP agents and ACP feed compliance for ChatGPT Instant Checkout.
What Does the Feed Analyzer Check?
Our product feed analyzer inspects your product pages for Schema.org JSON-LD Product markup and evaluates data quality across six key dimensions that AI shopping agents rely on to discover and recommend your products.
Product Titles
Validates product names are present, descriptive, and properly formatted for AI agent parsing.
Pricing Data
Checks for price, currency, price validity dates, and sale price information in structured data.
Images & Media
Verifies product images exist with valid URLs, proper formats, and sufficient resolution for display.
GTIN & Identifiers
Looks for GTIN/UPC barcodes, SKU numbers, MPN codes, and brand attribution in product markup.
Descriptions
Evaluates product description quality, length, and whether they provide meaningful information for AI agents.
Availability
Checks availability status (InStock, OutOfStock, PreOrder) and ensures AI agents get accurate stock data.
Why Product Data Matters for UCP & ACP
AI shopping agents are transforming e-commerce. ChatGPT, Google AI Mode, and Microsoft Copilot already help millions of users discover and purchase products. These agents depend on structured product data to make accurate recommendations.
- Missing prices: AI agents cannot recommend products without clear pricing data
- No identifiers: Without GTIN or SKU, agents cannot match or compare your products
- Poor descriptions: Vague descriptions lead to irrelevant AI recommendations
- Missing images: Products without images are deprioritized in AI shopping results
- No availability: Agents avoid recommending products with unknown stock status
- ACP feed compliance: ChatGPT uses ACP product feeds submitted via chatgpt.com/merchants — the same data quality standards (titles, descriptions, GTINs, pricing) apply. Our analyzer checks ACP feed compliance alongside Schema.org validation
Understanding Your Feed Score
The analyzer evaluates your product feed across four scoring categories. Each category measures a different aspect of your product data completeness and quality.
Core Data
Product names, pricing, currency codes, and availability status. These are the minimum fields AI agents need to display and recommend products.
Product Media
Image URLs, thumbnails, and media assets. Visual data helps AI agents present products in rich shopping experiences and comparison views.
Identifiers
GTIN/UPC barcodes, SKU numbers, MPN codes, and brand attribution. Identifiers enable product matching, price comparison, and inventory lookup.
Descriptions
Product description length, detail quality, and specification coverage. Rich descriptions help AI agents match products to customer intent.
How to Analyze Your Product Feed
Enter Product Page URL
Provide a specific product page URL from your store (e.g., mystore.com/products/example-product).
Select Sample Size
Choose how many products to analyze. Larger samples provide more representative quality scores.
Run Analysis
Click analyze to scan your product pages for Schema.org Product markup and evaluate data quality.
Review Scores
Examine your scores across Core Data, Product Media, Identifiers, and Descriptions. Focus on the lowest-scoring areas first.
Complete Your AI Commerce Setup
Frequently Asked Questions
What does the Product Feed Quality Analyzer check?
The analyzer examines your product pages for Schema.org Product JSON-LD markup and evaluates data quality across four categories: Core Data (product titles, pricing, availability), Product Media (images, thumbnails), Identifiers (GTIN, SKU, MPN, brand), and Descriptions (product descriptions, specifications). Each category receives a score that contributes to your overall feed quality grade.
Why does product data quality matter for AI commerce?
AI shopping agents like ChatGPT, Google AI Mode, and Microsoft Copilot rely on structured product data to discover, compare, and recommend products. Poor data quality means missing prices, vague descriptions, or absent identifiers, which causes AI agents to skip your products or provide inaccurate recommendations to shoppers.
What is Schema.org Product markup?
Schema.org Product markup is a standardized JSON-LD format embedded in your product pages that describes product attributes in a machine-readable way. It includes properties like name, description, price, availability, images, GTIN, SKU, and brand. Search engines and AI agents use this markup to understand your products.
How is the feed quality score calculated?
The feed quality score is calculated across four weighted categories: Core Data checks for product names, pricing, currency, and availability status. Product Media evaluates image URLs, dimensions, and thumbnails. Identifiers checks for GTIN, SKU, MPN, and brand information. Descriptions evaluates product description length and quality. Each product is scored and the results are averaged across your sampled products.
Should I analyze individual product pages or collection pages?
The analyzer works best with specific product page URLs rather than collection or category pages. Individual product pages contain the detailed Schema.org JSON-LD Product markup that the analyzer evaluates. Collection pages typically contain less structured data per product and may not include all the fields needed for a comprehensive quality assessment.
How do I improve my product feed quality score?
Focus on the lowest-scoring categories in your report. For Core Data, ensure every product has a title, price, and availability status. For Media, add high-quality product images with proper URLs. For Identifiers, include GTIN/UPC barcodes, SKUs, and brand names. For Descriptions, write detailed product descriptions of at least 50 characters. Then re-run the analyzer to verify improvements.
Does this check ACP product feed requirements?
Yes. The analyzer checks title length (150 chars max), description length (5000 chars max), GTIN presence, and currency format — all requirements for ACP product feeds submitted to chatgpt.com/merchants. ACP (Agentic Commerce Protocol) by OpenAI and Stripe powers ChatGPT Instant Checkout, and the product data standards closely mirror Schema.org Product markup.
Want Continuous Feed Monitoring?
Get automatic product feed quality monitoring, instant alerts when your data quality drops, and weekly reports tracking your feed health over time with a UCPtools subscription.
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