This is part 2 of our Agentic Protocol for Ecommerce series. 

Read Part 1: The Universal Commerce Protocol (UCP): What It Is and Why It Matters for Ecommerce

Read Part 3: Agent2Agent (A2A) Protocol: Enabling AI Agents to Transform Ecommerce

AI is getting very good at talking, but it still struggles with actually doing. If you’ve ever tried to use large language models (LLMs) like ChatGPT or Gemini for real business work, you’ve probably run into the same issue: it can answer questions, but it can’t reliably pull live inventory, check order status, update a CRM, or grab a file from Google Drive without being provided with documents in the chat or being directly integrated into the tool you need it to access. 

Thankfully, developers have been working hard to build solutions that make it easier for people to use AI tools across interfaces, programs, and apps. One of those solutions is Anthropic’s Model Context Protocol (MCP).

In this article, we’ll break down what MCP is, how it works, and why it matters for ecommerce merchants who are hungry for AI agents that can actually access their data and provide value for their businesses.

What is the Model Context Protocol (MCP)?

Introduced by Anthropic in late 2024, the Model Context Protocol is an open-source AI standard created to make it easy for developers to connect AI applications to external systems. In simpler terms, MCP defines a common “language” that lets AI models, like ChatGPT or Claude, safely communicate with outside data sources, services, and tools. As the use of AI agents and generative AI has grown, the need for an easy way to connect these systems to other tools has also increased. 

Think of it this way: with nothing but an LLM like ChatGPT, you could technically give it access to a codebase or documents in the chat, but in order for the AI tool to access that information, it’ll need to use a lot of its power and saved context just to review the information you provide it with before it can actually start working on your prompt, as is required with everything aside from what it already “knows” via training data. 

With MCP, an AI assistant isn’t limited to its built-in knowledge; it can retrieve fresh information or take actions by interfacing with your systems through a consistent protocol. MCP was created to bridge the gap between LLMs and the live data or actions they need from the real world. As Anthropic has explained, you can think of MCP as a “USB-C” port for AI applications. “Just as USB-C provides a standardized way to connect electronic devices, MCP provides a standardized way to connect AI applications to external systems.” 

MCP is great for saving context as it's essentially like plugging anything you need your AI to reference directly into it, rather than needing to use resources to gather the information itself. 

How Does MCP Work?

At a high level, MCP works through a client-server architecture that enables two-way communication between an AI and external systems. Imagine the AI is a person who speaks a certain language, and your database or service is another person who speaks a different language. MCP acts as a common translation layer so they can understand each other. This works via:

1. MCP Servers: These are connectors or endpoints that expose data or functionality from an external system in the MCP format. For example, you could have an MCP server for a platform API like Shopify, a code framework like Laravel, or third party service like Google Calendar.

2. MCP Clients: These live on the AI side and are often built into the AI application or agent. The client finds available MCP servers and helps the AI formulate requests and understand responses.

3. Standardized Messages: MCP uses a consistent message format to send requests and get responses. This standardization (in JSON-RPC 2.0) means an AI agent doesn’t need custom code for every new service; if both the AI and the service speak MCP, they can work together out of the box. 

Consider this example: You need to create new products in Shopify via API and would like an AI bot to do the task for you.

Without an MCP server, an LLM like Claude or ChatGPT must rely on its training data and/or crawl and fetch documentation from the web, then embed it in its data store. This training data can potentially be outdated, and the LLM’s ability to crawl live data from the internet is often severely hobbled for security purposes. This ultimately results in a lot of used up context and probable hallucinations for it to be considered as a “good” way to go about completing the task. 

By referencing the MCP server, the LLM will fetch an up to date set of capabilities for a given topic and will discover instructions on how to use them to best respond to its current query. As an added bonus, this will happen using a minimal amount of context tokens and significantly reduce, or altogether eliminate, hallucinations.

With MCP, we now have a standard way for third-party systems to define their capabilities for an LLM to consume in order to teach the LLM how to use their tooling to complete tasks efficiently. 

What Can MCP Enable? - Integration Examples and Use Cases

MCP unlocks a whole new range of capabilities for AI applications by giving them access to live data and the ability to perform actions. Here are some examples of what becomes possible when AI systems use MCP:

• Personalized AI Assistants: Agents can connect to your calendars, inventory systems, and CRM systems. For instance, an AI could check your inventory database for in-stock items or update a customer record in your CRM. With MCP, you don’t need to rely on your ecommerce platform or the tech you use to set up AI-driven integrations; simply link whatever programs you need to work together. 

• Automated Workflows: An AI could use MCP to chain multiple tools together. For example, it might fetch a design file from Figma, generate code, and then deploy that code to a server–all via MCP connections to those services. In an ecommerce context, imagine an AI that pulls product data, then calls a marketing tool to create a promotional campaign automatically.

• Real-Time Data Retrieval: Because MCP lets AI access current data, your AI customer support bot could retrieve the latest order status or shipping info for a customer, rather than replying with stale information. It can answer questions like “Is product X back in stock?” by directly querying your live inventory system through MCP.

• Complex Task Execution: Beyond data retrieval, MCP also enables AI to perform actions. For example, an AI agent could initiate a restock order with a supplier or schedule a delivery pickup by invoking the appropriate MCP-connected service. 

Instead of being stuck with pre-trained knowledge or having to find ways for your tools to link up and communicate with each other, AI assistants using MCP can connect to and communicate with all your platforms and services instantly, handling tasks like inventory management, marketing, sales, analytics, and more. 

Why Does MCP Matter?

AI technology is here to stay, and without protocols like these that make integration and interactivity between systems easy, we’re all left waiting for applications to work together to create their own links, or else we need to figure out how to do it ourselves. This is obviously not the easiest endeavour as it requires a lot of skill, time, and effort. 

For developers, the Model Context Protocol reduces development time and complexity when integrating AI solutions with new systems. You no longer have to write one-off integrations for each AI tool or each data source as long as you conform to the MCP standard. This also fosters a growing ecosystem of ready-made MCP connectors for databases, SaaS apps, etc., so developers can plug-and-play capabilities into their AI apps.

In terms of AI applications and agents, they gain access to an ecosystem of data sources, tools, and apps, thus naturally enhancing their capabilities and improving user experience. Similarly, end users benefit from more capable, smarter, and more helpful assistants that have access to larger pools of data than they would otherwise.

MCP for Ecommerce: A Game Changer for Merchants

In the first article of our AI protocol series, we broke down the Universal Commerce Protocol, built by Shopify, Google, and a handful of retail and ecommerce giants, with the goal of creating a standard for agentic commerce wherein users (customers) could successfully shop directly through all the AI-based programs and tools they interact with every day. 

A Model Context Protocol server essentially provides a live, structured data endpoint for your store. Instead of an AI guessing information from your website’s HTML, it can query an MCP endpoint that delivers exactly the data it needs (e.g. product descriptions/names, price, inventory count, descriptions) in a format that’s easy to consume. 

This means the AI’s answers about your products will be accurate and up to date (note that if you want to control transactions between the AI and your store, you’ll need to implement UCP). 

While implementing UCP in your store will increase its discoverability in the spaces where AI appears and is used by shoppers, MCP makes ecommerce easy in other ways, often on the backend side of your business. MCP will ensure that AI agents have a direct line to your store’s data and services, rather than relying on integrations, web scraping, or outdated feeds. 

It’s also important to note that MCP can act as a transport layer for UCP, as UCP capabilities can map 1:1 to MCP tools. A business may decide to expose an MCP server that wraps their UCP implementation, allowing LLMs to call UCP tools like create_checkout directly.

MCP could be used to fetch all sorts of context: product data, customer data, inventory levels, even non-commerce tools, like a shipping carrier’s system or a supplier database. It provides the underlying connectivity that makes an AI truly knowledgeable and capable within a merchant’s ecosystem. 

By leveraging MCP, you can effectively link all the tools you use for your business, such as Slack, Google Drive, CRM, ERP, and PIM. This level of easy AI integration in ecommerce can open up your entire operation to agentic AI tools that can actually bring value to your team, save you time, and surface insights you may have otherwise missed. 

Conclusion

The Model Context Protocol is a new part of how modern software will work moving forward, solving an increasingly important problem: getting AI access to the data and systems it needs to be useful.

For ecommerce businesses, that means fewer one-off integrations, more automation, and AI agents that can actually operate within your tech stack for once. If you’re already investing in AI, now’s the time to think about how “connectable” your systems are so you can be ready to open up your stack to AI agents. 

If you want help making your ecommerce stack MCP-ready, we at Blue Badger can help you plan the right integrations, keep it secure, and ensure your AI initiatives deliver real value. Get in touch with us today to learn more. 

Next: Agent2Agent (A2A) Protocol: Enabling AI Agents to Transform Ecommerce