What Is Model Context Protocol (MCP)?

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

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:

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:

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

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

Technical Requirements from a PIM Like Pimberly

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.