Understanding the Model Context Protocol (MCP)

By now, you’ve probably heard the term “Model Context Protocol,” or MCP, in conversations about AI. In simple terms, MCP is a new open standard that allows AI systems such as chatbots and AI agents to connect with external data sources, tools, and software services through a single, unified interface.

It was introduced in 2024 to address a common issue. Every time an AI system needed access to a new application or database, developers had to build a custom integration. Each service had its own interface, which meant nothing worked out of the box. This created delays, increased maintenance, and made it hard to scale. MCP changes that by providing a universal protocol that lets AI models access tools and data using a shared format.

This is similar to how open standards like HTTP or REST APIs allowed web services to communicate across systems. MCP brings that same standardization to AI applications.

You can also think of MCP as a kind of USB-C for AI. Just as one type of port lets you connect many different devices without using a new cable for each one, MCP gives AI models a consistent way to connect with a wide range of tools. As long as both sides speak MCP, they can work together without needing custom code or manual setup.

Because it is an open standard, no single company controls MCP. It is being developed in the open by contributors across the industry. Major AI platforms, including tools from OpenAI and Google, are already adding support. This makes it more likely that MCP will become a long-term standard that many systems can rely on.

MCP is built on a simple idea. It uses a client-server model to connect AI systems with external tools and data sources. In this setup, the AI application such as an AI agent or assistant acts as the client. Each external system is connected through a small piece of software called an MCP server.

The MCP server acts as a translator. It knows how to communicate with a specific service, like Slack, Salesforce, or a SQL database, and it can pass information back to the AI in a format the AI understands. This makes it possible for the AI to ask for data, send instructions, or take action inside another system without needing to know how that system works internally.

For example, imagine your AI agent needs to pull customer data from your CRM or search through files in Google Drive. The agent sends a request to the appropriate MCP server. That server handles the details of working with the external service’s API and then returns the results in a clean, structured format the AI can use.

From the AI’s point of view, every service looks the same. Whether it is calling a database, sending a message in Slack, or accessing a document store, the interaction follows the same pattern. The AI makes a request using MCP, and the response arrives in a consistent structure.

This is what makes MCP powerful. Any tool or data source that has an MCP server can work with any AI agent that understands the protocol. There is no need to build a separate integration for each new system. If someone has already built an MCP connector for a tool like Slack or Salesforce, your AI agent can start using it right away. That means less time writing custom code and more time focusing on the actual workflow you want to automate.

If you operate a system or application, such as a customer platform, internal tool, or data source, and you want to enable AI agents to interact with it, you will need to set up your own MCP server.

An MCP server acts as a bridge between your system and any AI agent that supports the Model Context Protocol. It handles the technical work of receiving requests, pulling data from your system, and returning that data in a standard format. To do this, a developer needs to understand your system’s API or backend and build a connector that follows MCP's rules. This usually involves handling authentication, formatting data correctly, and managing what information can be shared with the AI.

While setting up an MCP server takes some technical work, the long-term benefits can be substantial. Once your system is connected through MCP, it becomes much easier to build AI agents that can retrieve data, perform tasks, or monitor events within that system. You no longer need to create a new integration for every AI use case or model update. Instead, you maintain one standard interface that works across many tools.

For organizations that rely on custom workflows or internal platforms, this offers a cleaner and more scalable path forward. It gives your AI projects a stable foundation and makes it easier to roll out new capabilities without constantly rebuilding integrations from scratch.

To appreciate MCP’s value, consider how things worked before this standard came along. Integrating an AI agent with external services used to be a major headache. Every service had its own unique API and data format, so engineers had to write custom code for each connection and teach the AI how to handle each service’s quirks. If one of those services changed (even slightly), it could break the entire workflow, requiring more patches and troubleshooting. 

There was also a lot of heavy lifting in terms of maintenance: managing different authentication methods, chaining outputs from one tool into inputs for another, and handling countless edge cases where things could go wrong. 

In short, connecting an AI to five different tools often meant building five separate integrations from the ground up. This was a slow and error-prone process.

With MCP, many of these pain points disappear. Instead of reinventing the wheel for every new data source or app, developers can rely on a common protocol and a library of pre-built connectors. 

The MCP approach makes tool integrations plug-and-play, rather than requiring bespoke coding for each service. Your AI agent can “plug into” a new system (say, a CRM or an ERP module) just by adding the appropriate MCP connector, immediately giving it the ability to fetch data or perform actions in that system. All the translation logic is handled by the MCP server, so the interaction is consistent and robust. 

This not only speeds up development, it also leads to fewer issues over time. If a tool updates its API, the MCP connector can be updated in one place, and all AI agents using it benefit right away. 

Overall, using MCP shifts integration work from ad-hoc scripts to a stable, standardized layer, making it much easier to build and maintain complex AI workflows.

The Model Context Protocol may sound technical at first, but its purpose is clear. It makes it easier and more reliable to connect AI systems to the tools and data your business already uses. 

By creating a standard way for AI agents to interact with different systems, MCP removes much of the friction that typically slows down AI projects. Instead of spending time and budget on building and maintaining one-off integrations, teams can use existing connectors and focus on solving real business problems.

MCP is not a shortcut to AI adoption. You still need strong use cases, the right skills, and a clear plan. But MCP does help reduce complexity. A good way to think about it is like installing a standard railway track. Once the track is in place, you can run many different trains without rebuilding the route every time. The same principle applies here. With MCP in place, new AI use cases can be developed and deployed much faster.

For decision makers, the message is simple. As you work on your AI roadmap, pay attention to open integration standards like MCP. They can help speed up delivery, reduce technical overhead, and make your AI systems easier to scale and adapt over time.