Model Context Protocol Explained: A Game-Changer for AI

Mariia Yuskevych -

What’s the biggest challenge for developers working with AI models? It’s not getting the right data, as there are plenty of databases to rely on. It’s making AI actually use it.

Imagine you're building an AI assistant for a sales team. You need it to understand your company’s products, sales process, and customer interactions. But there’s a problem: AI models don’t automatically connect to external data sources like databases or APIs. Every new source requires a custom setup, making it time-consuming and complicated.

This is where Model Context Protocol (MCP) comes in. It allows AI to easily access and update external information without extra work. Developers no longer need to build complex integrations because MCP makes sure AI tools stay connected and useful.

What is Model Context Protocol?

Think about how a USB port works. No matter the device, headphones, a mouse, or a keyboard, if it has the right plug, you can connect it to your computer without needing a different port for each one. The Model Context Protocol (MCP) works in a similar way, but for AI models. It provides a standard way to connect various tools, data sources, and applications to large language models (LLMs), ensuring they can access the right information without complicated custom integrations.

At its core, MCP is an open standard developed by Anthropic. It acts as a universal bridge between AI systems and external data, whether that’s company knowledge bases, business tools, or developer environments.

Instead of manually building a custom connector for every new data source, MCP creates a common set of rules for how AI systems interact with information.

A protocol is simply a set of rules that dictate how data is formatted and exchanged between systems. MCP follows this principle, ensuring that LLMs can pull in relevant data in a structured and secure way. 

With MCP, developers have two main options: they can expose their data by setting up an MCP server, or they can build AI applications (MCP clients) that connect to these servers. This flexibility allows AI to become more context-aware, making interactions more useful and relevant across different domains.

What are the Main Components of the Model Context Protocol?

To simplify adoption, MCP is built around three key components:

1. MCP Hosts

These are AI-powered applications, such as Claude Desktop or IDE plugins, that initiate connections and request data. Hosts act as the central hub where users interact with AI, pulling in relevant information from external sources.

2. MCP Clients

Clients serve as intermediaries between hosts and servers. Each client manages a secure, one-to-one connection with a specific server, ensuring that data flows efficiently without interference. Clients are embedded within the host application.

3. MCP Servers

Servers provide context, tools, and prompts to clients. They connect to various data sources, like local files, databases, and online services, making critical information accessible to AI applications. Your data can be accessed from cloud storages like Google Cloud, other sources like GitHub, offline files, say PDFs, or even Slack or Google Maps.

By standardizing these three components, MCP simplifies the way AI systems integrate with external data, reducing complexity for developers.

You can learn all the specifications on the Model Context Protocol website.

How can Developers Use the Model Context Protocol?

At its core, MCP operates through JSON-RPC over HTTP or stdin/stdout. When an AI application needs a tool, it sends a request to the MCP server, which responds with available tools. The AI can then invoke these tools dynamically, executing actions or retrieving data as needed. This flexible approach makes it easy to expand AI capabilities without frequent updates to the host application.

Microsoft Copilot Studio utilizes MCP to connect AI-powered agents with enterprise tools while ensuring security and governance. Here’s how the process works:

  1. Setting up an MCP server: A company builds an MCP server that hosts tools and capabilities. This server communicates using JSON-RPC, responding to requests for available tools and executing actions when called.
  2. Connecting via a secure infrastructure: The MCP server is made available to Copilot Studio through connector infrastructure, ensuring compliance with enterprise security policies like Virtual Network integration and authentication controls.
  3. Fetching available tools: When connected, Copilot Studio automatically retrieves a list of tools from the MCP server. These tools are then integrated into the AI agent, inheriting their descriptions, input parameters, and outputs.
  4. Using MCP tools in Copilot:Users can access these tools directly in Copilot Studio by selecting an action. The AI agent can call the tools as needed, providing real-time access to external data or functions.
  5. Automatic updates and maintenance: If a tool is updated or removed on the MCP server, these changes are automatically reflected in Copilot Studio. This eliminates the need for manual updates and reduces the risk of outdated or broken integrations.

This integration helps developers quickly adapt AI agents or other AI tools without rebuilding the entire app every time a new data source is added.

Consider Perpetio Your Trusted Partner

Our developers have extensive experience working with AI models and tools. We’ve built solutions like an AI-powered cookbook that suggests recipes based on user preferences, such as ingredients, cuisine type, and dietary restrictions, and Dreamota, an app that interprets users’ dreams using natural language processing.

We’re already experimenting with integrating the Model Context Protocol into internal prototypes and are fully prepared to apply this technology in commercial projects. Whether you're exploring enterprise automation or building smarter AI assistants, our team is ready to help.