Posted by Pat Tully Published: Jun 26, 2025 Updated: Jun 30, 2025

What Is Model Context Protocol (MCP)?

Model Context Protocol (MCP) is a framework for structuring product data and its contextual relationships so AI agents—like ChatGPT, Amazon Rufus, or Google Gemini—can interpret and act on the information reliably, accurately, and in line with your brand’s intended logic. As AI-driven commerce and customer experiences rise, MCP becomes a critical layer to ensure AI systems don’t just access your product data—but understand it within the correct context.

Pat Tully

Sr. Content Marketing Manager

What Is MCP in the Context of AI?

Model Context Protocol ensures that product data:

Is context-aware – Beyond static attributes, the data includes logic, relationships, and constraints (e.g., “This product is incompatible with X,” or “Only available in region Y”).
Adheres to ontologies – Uses shared definitions and taxonomies across systems (like GS1, Schema.org, or your custom product models).
Is governed by logic – Rules define how AI agents interpret variants, bundles, dependencies, and conditions.
Is optimized for AI workflows – Enables agents to simulate real-world scenarios like compatibility checks, upsells, or bundle recommendations.

Why MCP Matters for AI Agents

As AI agents like Google SGE, ChatGPT plugins, and Shopify Sidekick evolve, they:

Move beyond simple attribute-matching toward decision-making.
Need rules and logic to avoid misinformation (“does this toner fit my printer?”).
Benefit from machine-actionable instructions to simulate human expertise.
Require consistent product graph logic across feeds, APIs, and surfaces.

Without MCP, AI might misinterpret your product relationships, surface irrelevant bundles, or apply promotions inaccurately.

How to Make Your Data Context-Rich for AI Agents

Area	Action	Tools/Tech
Context Modeling	Define bundles, variants, compatibilities, exclusions	Product Graphs, Ontologies
Semantic Relationships	Use linked data formats to express dependencies	JSON-LD, RDF, OWL, GS1
Logic Integration	Encode “if-then” rules (e.g., requires batteries)	PIM Rules Engine, AI Layer Logic
Feed Customization	Tailor feeds by context (e.g., regional availability, regulated SKUs)	Syndication Rules in PIM
Dynamic FAQs & Guidance	Expose decision trees and compatibility Q&A	NLP-ready content, FAQ markup
API Governance	Version APIs that reflect product model changes	Versioned REST, GraphQL
Schema Extensions	Extend Schema.org with context-specific properties	Custom Schema.org types

How to Test If You’re MCP-Ready

You can evaluate Model Context Protocol readiness with:

Schema.org extensions: Do your pages include contextual properties (e.g., isAccessoryOrSparePartFor, isVariantOf, compatibleWith)?
Structured Q&A and logic: Do you offer decision-support flows AI can parse?
Feed granularity: Can your feeds reflect different contexts (locale, language, inventory)?
Simulate an agent’s view: Use OpenAI’s browsing plugin or tools like Sidekick to test comprehension.

Pro Tip

If you’re using a composable PIM like Pimberly, you can model product context and logic in the backend, then output feeds or APIs that AI systems can ingest directly—tailored to the rules you define.

Feeding Contextualized Data to AI Platforms

AI platforms don’t just read product specs—they simulate intent. Here’s how to give them structured context:

1. ChatGPT (OpenAI)

How It Understands Context:

Via plugins that rely on logic-aware APIs.
Through structured Schema.org with rich relationships.
From GPTBot crawling content with embedded FAQs and logic trees.

How to Feed It:

Method	What to Do
Schema.org + Logic	Use `isVariantOf`, `compatibleWith`, `accessoryOf` etc.
Expose Rules via APIs	Deliver decision trees, guided flows, or bundled logic via endpoints
Enable Plugin Context	Build plugins that allow users to ask “Will this work with…?”
GPTBot Discovery	Allow access to context-aware pages; use rich markup and conversational Q&A

2. Google Gemini

How It Understands Context:

Leverages Google’s Shopping Graph and Merchant Center feeds.
Uses Schema.org to infer relationships and logic.
Aggregates intent-rich snippets and structured Q&A.

How to Feed It:

Method	What to Do
Feed Logic-Enhanced XML	Tag variants, bundles, and options with identifiers and logic
Add FAQs + Semantics	Use structured FAQ markup and Schema.org relationships
Configure Rules in GMC	Enable dynamic pricing, availability, and variants per region

3. Other Platforms (Amazon Rufus, Shopify Sidekick, Microsoft Copilot)

Platform	Feed Method
Amazon Rufus	Structured data in Amazon Seller Central, including bundles and cross-sells
Shopify Sidekick	Metadata-rich descriptions with AI-ready fields; PIM-fed compatibility logic
Microsoft Copilot	Index via Bing using full Schema.org + structured product graphs

Pimberly Use Case

Customers using Pimberly’s PIM/DAM solution can:

Model contextual product data (e.g., region-based availability, accessory compatibility).
Automate variant relationships and generate Schema.org + feed logic.
Publish APIs or feeds tailored to ChatGPT, Shopify Sidekick, and other AI agents.

The Checklist for MCP Compliance

Step	Action
1	Model product relationships (bundles, variants, accessories)
2	Add Schema.org context (e.g., `isVariantOf`, `compatibleWith`)
3	Expose Q&A and guided logic via markup or APIs
4	Feed enriched product data to platforms like Google, Shopify, Amazon
5	Enable GPTBot and other AI crawlers to index context-rich pages
6	Maintain feed logic for localization, stock, and compliance

Connecting Pimberly to AI Agents for MCP

There are three primary ways Pimberly users can implement Model Context Protocol for generative platforms:

1. Custom ChatGPT Plugin (Context-Aware)

Best For: Real-time product assistance and compatibility checks

Create REST APIs for search and logic-based queries.
Include metadata like isAccessoryOrSparePartFor in responses.
Provide guided search or Q&A endpoints for ChatGPT to surface.

2. AI-Readable Product Pages with Embedded Logic

Best For: SEO + AI surfacing across all major engines

Use Pimberly templates to include Schema.org logic in HTML pages.
Publish FAQs and structured buying guidance.
Ensure GPTBot and other crawlers can access.

3. Feed-Based Context Syndication

Best For: Large-scale, logic-aware partner integrations

Publish context-annotated product feeds (e.g., “This charger only works with…”).
Share feeds or APIs with Sidekick, Klarna, OpenAI, or TikTok.

Examples from Other Brands

Company	How They Use MCP-Style Context
Klarna	Offers real-time product matching and compatibility validation
Instacart	Enables logic-driven substitutions in real time
Shopify Sidekick	Uses natural language and metadata to surface product relationships
Expedia	Surfaces bundles and upgrade options with structured decision-making logic

Technical Requirements from a PIM Like Pimberly

Component	Status in Pimberly	Action
Product Graph Modeling	Available	Define variants, dependencies, regional rules
Schema Extensions	Templatable	Add logic fields to HTML exports
Feed Exports	Built-in	Publish feeds with MCP logic and relationships
Custom APIs	Supported	Enable /getCompatibles, /getBundles, etc.
Syndication Rules	Out-of-the-box	Tailor exports by locale, language, compliance rules

Final Thought: A Strategic Layer for the AI Era

Model Context Protocol is more than a technical layer—it’s a strategic advantage in the era of AI-native commerce.

By embedding logic, relationships, and context into your product data, you ensure:

✅ Accurate AI recommendations
✅ Smarter bundling and upsells
✅ Reduced customer confusion
✅ Higher conversion through intelligent automation

Whether through plugins, structured pages, or syndicated feeds—MCP lets your product data speak the language of AI.

Take a deeper dive

In a personalized demo of the platform, you’ll see how you can:

Easily create, enrich and automate complex product data, images, and videos.
Drive sales and deliver best-in-class online experiences.
Increase revenue by reaching new channels and markets with confidence in your data.
Give your teams a central hub to manage and update product data.

Book a Demo