What Is MCP? Model Context Protocol Explained

Key Takeaways

• MCP (Model Context Protocol) is an open standard Anthropic introduced in November 2024 for how AI agents connect to external tools, databases, and systems. 

• In December 2025, Anthropic donated MCP to the Agentic AI Foundation, a directed fund under the Linux Foundation. This keeps MCP open and vendor neutral as adoption grows. 

• MCP and UCP (Universal Commerce Protocol) are not competing standards. MCP is the data access layer that lets agents read from your systems. UCP is the commerce execution layer that lets agents complete a purchase.  

• Your product data quality determines whether an AI agent using MCP finds accurate, complete information about your products, or skips them for a competitor’s instead. 

What is Model Context Protocol? 

Model Context Protocol (MCP) is an open standard Anthropic created and released in November 2024. It standardizes how AI agents connect to external tools and data sources, things like databases, APIs, file systems, and business software.

Think of it as a universal adapter. It lets an AI agent plug into any compatible external system without a custom integration built for every single connection. 

Before MCP existed, every AI application needed its own bespoke integration with every external tool it wanted to use. That approach didn’t scale. MCP replaced it with a shared language. Any MCP compatible AI agent can now connect to any MCP compatible server once, then reuse that connection everywhere. 

MCP has grown quickly since its release. In December 2025, Anthropic donated the protocol to the Agentic AI Foundation (AAIF), a directed fund under the Linux Foundation. The foundation was co-founded by Anthropic, Block, and OpenAI, with support from Google, Microsoft, AWS, Cloudflare, and Bloomberg. 

How MCP Works

MCP works through two components, and neither one requires technical knowledge to understand at a business level. 

MCP servers are software components that expose a specific system’s data or capabilities to AI agents. A retailer’s MCP server, for example, might expose its product catalog. That lets an AI agent search products, read attributes, or check availability in real time. It works from governed product data rather than scraped or cached web content. 

MCP clients are the AI agents or applications that connect to those servers. Claude, ChatGPT, Gemini, and Microsoft Copilot are all examples. 

Here’s what that looks like in practice. 

1. A shopper asks their AI assistant to find a compatible replacement filter for their air purifier. 
2. The agent uses MCP to connect to a retailer’s product server and queries the catalog for compatible filters.
3. The agent reads the compatibility attributes and returns the correct result accurately and in real time.

Without MCP, that same agent would have to scrape the retailer’s website and guess at compatibility from unstructured content, which is far less reliable. 

How MCP Differs From UCP

MCP and UCP are mentioned together constantly, so it’s worth clearing up the confusion directly. UCP, short for Universal Commerce Protocol, is an open standard for how AI agents shop. It covers everything from product discovery and cart building to completing a purchase. UCP and MCP aren’t competing standards. They operate on different layers of the agentic commerce stack and are designed to work together.

MCP is general purpose. It was not built specifically for commerce, and it handles a huge range of AI agent use cases beyond shopping. UCP was built by Google, in collaboration with Shopify and other retail partners. It standardizes how agents complete a purchase once they’ve found what they’re looking for. 

A simple analogy helps here. MCP is the pipe. UCP is the commerce rail that runs through it. An AI agent might use MCP to access a merchant’s product data, then use UCP to complete a purchase based on what it found. For a deeper breakdown of how the two protocols interact, see our full MCP vs UCP guide 

Why MCP Matters for Your Product Data 

This is where MCP stops being an abstract standard and becomes a business problem. When an AI agent connects to your systems through MCP, it reads your product data directly. That means your attributes, descriptions, specs, availability, and pricing. The quality of that data determines the quality of what the agent tells the shopper. 

Complete, well-structured product data gives an AI agent everything it needs to match your products to buyer intent correctly. That means accurate specs, filled attributes, and clear descriptions. Incomplete or inconsistent data means the agent either skips your product, surfaces inaccurate information, or makes a recommendation that leads to a return. 

This isn’t hypothetical. McKinsey’s research on AI search found that a brand’s own website typically supplies only 5 to 10% of what generative AI tools draw on to build an answer, with the rest pulled from affiliates, reviews, and other third-party content. Source mix varies by category, though, and ecommerce leans more heavily on brand and retailer sites than most sectors do. That means your own product data carries outsized weight in what an AI agent tells a shopper. If that data isn’t accurate and complete, the agent has little else reliable to work from.

That gap is growing more consequential by the month. Adobe Analytics found that AI-referred traffic to US retail sites was up 693% year over year during the 2025 holiday season alone. A PIM is the system that governs product data before it reaches any channel, including an MCP server. Connecting a PIM to an MCP server means AI agents access governed, enriched, current PIM data rather than scraped or cached content. That’s the difference between an AI agent recommending your product correctly, and recommending a competitor’s instead. 

What MCP Readiness Means for Your Business

Most eCommerce businesses won’t implement MCP themselves. Their platform, whether that’s Shopify, BigCommerce, an ERP, or a PIM, handles that layer. What a business controls is the quality of the product data those connections expose. In practice, MCP readiness means:

• Product attributes are complete, with no missing dimensions, blank spec fields, or products without images
• Data is consistent across channels, so the same product is described the same way everywhere an AI agent might read it
• Product relationships are modeled, including compatible accessories, variant logic, and bundle components, so an agent can make an accurate recommendation instead of guessing
• Data is current, with pricing, stock levels, and availability up to date in the system an MCP server reads from

This is, in practice, exactly the work a PIM does, centralizing and governing the data that ends up in front of an AI agent.

MCP and Pimberly

A PIM sits at the center of MCP readiness. It’s where product data gets centralized, enriched, and governed before it reaches any channel, including AI agents connecting through MCP. A well-structured, complete product catalog feeding into an MCP server gives AI agents the governed, accurate data they need to surface and recommend your products correctly, rather than working from whatever they can piece together elsewhere. 

That’s the role Pimberly’s AI-powered platform plays for eCommerce teams already thinking about MCP and agentic commerce, governing product data before any AI agent, MCP-connected or otherwise, ever reads it. 

FAQs

Q: What is Model Context Protocol (MCP)?
A: MCP is an open standard, created by Anthropic and released in November 2024, that governs how AI agents connect to external tools, databases, and systems. It is not a product data structuring framework. 

Q: Who created MCP?
A: Anthropic created MCP and released it in November 2024. In December 2025, Anthropic donated the protocol to the Agentic AI Foundation, a directed fund under the Linux Foundation, to keep it open and vendor neutral as adoption grew. 

Q: What is the difference between MCP and UCP?
A: MCP is the data access layer that lets AI agents read from external systems. UCP is the commerce execution layer that lets agents complete a purchase. MCP is general purpose, while UCP, built by Google and Shopify, is commerce specific. The two are designed to work together, not compete. 

Q: What is an MCP server?
A: An MCP server is a software component that exposes a specific system’s data or capabilities to AI agents. A retailer’s MCP server, for example, might expose its product catalog so an agent can search products or check availability. 

Q: Does MCP affect how AI agents find and recommend products?
A: Yes. When an AI agent connects to a retailer’s systems through MCP, it reads that retailer’s product data directly. Complete, accurate data increases the chance an agent recommends the product correctly. Incomplete data increases the chance the agent skips it or gets something wrong. 

Q: Do I need to implement MCP for my eCommerce store?
A: Most businesses don’t need to build MCP infrastructure themselves. Platforms like Shopify, BigCommerce, and PIM systems handle that layer. What businesses control, and should focus on, is the quality of the product data those systems expose. 

Q: How does a PIM help with MCP readiness?
A: A PIM centralizes, enriches, and governs product data before it reaches any channel. That means complete attributes, consistent descriptions across channels, modeled product relationships, and current pricing and availability. That’s exactly what an MCP server needs to give AI agents reliable information. 

The Protocol Layer Isn’t Where the Work Is

Most of what MCP requires from a business is already handled by the platforms and tools it already uses. What determines outcomes in AI-driven discovery is the quality of the product data those protocols expose to AI agents. 

The brands winning in AI-driven discovery are the ones whose product data is complete, structured, and governed in a PIM. It’s ready to be read accurately by any AI agent that connects to it, whether that’s through MCP, UCP, or whatever comes next. That’s the foundation Pimberly is built to provide.