The wave of new ways to use AI has brought with it another avalanche of acronyms to heap onto the pile. But there’s one in particular that keeps coming up: MCP.
If you’re plugging AI applications like Gemini into an external platform, you will have used MCP. In this blog we’ll cover what MCP is, what it’s used for, and what opportunities MCP opens up to marketers. Let’s get into it.
In literal terms, MCP is a protocol - a set of rules that developers follow when building an MCP server. These rules describe how an external system should expose actions and data so an AI application can use them safely.
MCP is a standardized framework for AI applications (such as ChatGPT, Claude, Gemini, etc) to connect with external tools, data sources, and systems in a safe, standardized way. Think of it as a plug-and-play protocol for AI applications, so that instead of every AI application having to build custom integrations, they can all use the same framework.
If you want to connect an AI application an external system, that connection will rely on MCP to allow the two systems to understand one another, and connect in a secure way. MCP is an international standard in the same way that Bluetooth or USB-C are.
It’s a fair question, as APIs have been a fundamental part of how marketers connect sources, but they aren’t designed for AI applications. An AI applicationcan’t just plug into an API directly and know how to use it. It needs rules, and it needs them in a very specific structured format if you want it to perform very specific tasks. Otherwise you’ll be prompting til the cows come home.
For the uninitiated, an API is a set of rules that allows information to pass between two platforms. An API is what allows you to pay via PayPal on a website - it's basically like a port into the platform. It’s still a key part of connecting AI applications with external systems, but it needs an extra layer to work, and that’s an MCP server.
Once you’ve figured out what actions you want your AI application to have access to, you define those actions inside your MCP server following the Model Context Protocol. The MCP server for your external system can then run those actions using the rules you’ve implemented, and it will use the API under the hood to actually perform them.
Let’s get our definitions straight:
- An API is a set of rules that allows information to pass between two platforms. It’s the actual interface of the system.
- MCP is a protocol that allows AI applications to connect to external tools and systems through an MCP server.
- The MCP server is the layer that makes that external system’s interface usable by an AI application.
The difference is that while an API is a set of rules for another application to follow, MCP is the way selected capabilities of that system can be expressed to an AI application through the MCP server.
In order to connect an AI application to an external system, here’s the gist of how it works:
1. Write an MCP server - this is a programme that acts as a wrapper for the external system you want to connect to.
2. Write the code to run through the MCP server - define the system’s actions using MCP’s structured JSON definitions, including what actions are allowed, and which authorizations should be enforced. In most cases, this code calls the external system’s API under the hood to actually perform those actions.
3. Make the actions accessible - your MCP server publishes the allowed actions following the Model Context Protocol over a WebSocket connection, so AI applications can discover and understand what your external system can do.
4. Connect your AI application - Allow an MCP-aware AI application to connect and call those actions.
So, once everything is connected, here’s how requests come in from the AI application:
And similarly, information is sent back through the same chain for the user to see.
AI is getting more complex, but also more personalized. Instead of generic prompts, teams want AI applications to perform very specific tasks that only make sense inside their own data environment.
MCP makes this possible by giving data teams a standard way to connect any AI application to their warehouse, marketing tools, and internal systems so the AI can work with real data and produce far more relevant outcomes. Once you’ve built the MCP server for the external system you want to connect to, you can use it with any MCP-aware AI application. You don’t need to rebuild the integration every time you switch tools.
Beside being useful for hyper-specific AI use cases, MCP also lowers the barrier to data maturity by letting less technical marketers query their data in plain language using the AI tools that they already use, while the MCP server enforces authorizations and data governance guardrails.
MCP marks an important shift in how teams will use AI. The value of any AI application depends on its access to the right data and systems, and MCP is what finally makes that access structured and safe. As AI becomes more embedded in day-to-day decision-making, the teams who adopt MCP will be the ones able to move beyond generic outputs and into genuinely useful, business-specific intelligence.