Feedmind
How it’s scored →
FeedmindAI readiness scorecard
tonyschocolonely.com · 13-page sample
B+
83/100
Feedmind Score

The Feedmind Score measures how readable your catalog is to an AI assistant.

Dimension scores
Dim 01 · ACCESSIBILITY
Agent accessibility100
Assistant crawlers reach and read your pages without JavaScript.
Dim 02 · STRUCTURED DATA
Structured data30
Product markup is present but has no Offer, so price and availability are not machine-readable.
Dim 03 · ANSWERABILITY
Content answerability100
Descriptions carry the concrete facts buyers actually ask about.
Dim 04 · LANGUAGE PARITY
Multi-language parity100
Every locale reaches the same depth and is exposed via hreflang.
Top 2 issues to fix first
01
Product markup is present but has no Offer, so price and availability are not machine-readable.
Add an Offer node with price, priceCurrency and availability to each Product.
high
02
No GTIN (global product identifier) found on the page.
Map GTIN/EAN from your PIM into the Product markup so assistants can match your item to known products.
high
How it’s scored · FM-score methodology v1.0Full methodology →
Dimension weights
Agent accessibility25%
Structured data25%
Content answerability30%
Multi-language parity20%
Grade bands
A85 — 100
B65 — 84
C50 — 64
D35 — 49
Fbelow 35
Bands split in thirds for +/−.
Connect your Shopify store — full-catalog scan + fixesOr book 15 min →
tonyschocolonely.com · scanned 01 AUG 2026 · FM-score methodology v1.0Feedmind Score · How it’s scored →
Scores reflect a 13-product sample · a connected store is scored across the full catalog
++Step 02 · locked
Step 02 · AI simulation
See the buyer questions AI assistants can’t answer about tonyschocolonely.com
We run six real purchase questions against your product pages and show exactly where the assistant gives up. Your score is already yours — this part takes an email.
One email, one scorecard. No sequence, no reselling.
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