What Are MCPs? All You Need To Know

By Jemish Sataki

Overall Rating

Overview

Have you seen Apple TV’s most-watched show, Severance?

In Severance, employees at Lumon undergo a surgical procedure that splits their consciousness into two identities—the “innie” (at work) and the “outie” (outside work). These two selves are completely separate from each other, with no memory or communication crossing the boundary.

Take Mark Scout, for example. He grows increasingly desperate to send a message to his innie, a secret about his wife. However, Lumon's protocols and surveillance are so advanced that every attempt fails.

Now, imagine if a group of ex-employees created a protocol that bridges the two worlds, letting the innie and outie send secure messages and trigger actions without exposing sensitive data or alerting Lumon.

Something similar already exists in the real world of AI. It's called the Model Context Protocol (MCP), a way for AI agents to communicate securely with external systems.

Let’s explore what MCP can really do!

TechDogs-"What Are MCPs? All You Need To Know"

Have you been using any Generative AI tools recently?

Yes, they are great but often have some limitations. They may not be helpful in all real-world tasks since they are trained on limited data, which means limitations in the content they generate. Gen AI tools need more context and computing power to trigger actions in a way that truly pushes the boundary of their abilities.

That’s where Model Context Protocol (MCP) comes in. It gives AI tools extra context and lets them take actions in other apps, such as sending messages, developing workflows, or creating documents from scratch.

So, how can you build one? We will get to that, but first, let’s understand the Model Context Protocol.

What Is Model Context Protocol (MCP)?

Model Context Protocol (MCP) is like a two-way bridge between AI assistants and the enterprise tools you use every day. It doesn’t just help AI understand information, but it also helps it to take meaningful actions. That means your AI assistant can do more than just respond; it can actually do tasks based on your request.

Built as an open-source protocol, MCP helps connect AI to tools like CRM systems or development servers. So, instead of switching between apps or updating records manually, the AI can fetch data, send updates, or even trigger a deployment.

You can think of MCP as a kind of universal translator. Large language models (LLMs) are great with words but don’t naturally “get” how tools like GitHub or databases work. MCP fills that gap, giving AI access to tools using one standard, simplified language.

To better understand how MCP works, let’s break down its core components and how they interact.

The General Architecture Of MCP

At its core, MCP operates on a client-server architecture. A host application connects to multiple servers, allowing it to interact with various tools and data sources. The host acts as the central point, while each server manages specific resources or services.

MCP Hosts

Programs like Claude Desktop, IDEs, or other AI tools that need to access data through the MCP protocol.
MCP Clients

These are the components that connect directly to MCP servers. Each client has a 1:1 relationship with a server, meaning it can only connect to one server at a time.
MCP Servers

Small, efficient programs that provide specific features or capabilities, accessible through the Model Context Protocol.
Local Data Sources

Files, databases, or services on your computer that MCP servers can securely access to fetch or update data.
Remote Services

External systems on the internet (like APIs) that MCP servers can connect to, allowing access to services such as Google Calendar or GitHub.

Now that we understand the critical elements of MCP, let’s understand how it works!

How Does MCP Work?

MCP provides a clear set of rules that lets AI assistants easily talk with external tools, services, and databases. Instead of creating custom integrations for every app or service, MCP outlines a simple, standard way to structure requests, discover available actions, and connect securely. This helps developers quickly build reliable, two-way connections between AI models and everyday tools.

Think of MCP like HTTP, the protocol that powers the internet. HTTP lets you open websites from Chrome, Safari, or even a terminal window because all browsers understand the same rules. Similarly, MCP aims to be the universal "language" that AI uses to interact seamlessly with various software and data sources, regardless of their differences.

The interesting point is that MCP goes beyond just standardizing communication. Since AI understands language and intent, MCP presents structured options that AI can intelligently choose from. So, whether you ask your AI assistant to "open techdogs.com" or "fetch pictures of my dog," MCP helps it pick and use the right tool every time.

Now that we understand how MCP works at a functional level, let’s break down its journey over time, from prompt to response through the MCP lifecycle. Dive in!

The MCP Lifecycle

The MCP lifecycle has two main flows: the Connection and Messaging Lifecycle.

Connection Lifecycle

The MCP Host (like Cursor) starts by reading its MCP configuration file, which tells it which MCP Servers it needs to connect to.
The MCP Host sends an "initialize" request that includes its protocol version and capabilities.
The MCP Server responds by sending back its own version and capabilities, such as what tools it can offer.
Once everything is aligned, the MCP Client notifies the user that the connection is initialized and ready to go.
The connection is now live and ready to be used.

Messaging Lifecycle

When the user sends a prompt through the MCP Host, the system runs an LLM model to decide if a tool needs to be used. For example, when you type “What does TechDogs do?,” the system detects that you’re asking for information about TechDogs and knows the MCP server can help.
The MCP Client sends a message to the MCP Server to open the website, passing the necessary details (such as the URL).
The MCP Server processes the request, connects to techdogs.com, and retrieves the webpage.
The MCP Host then takes the server’s response (the webpage), merges it with the LLM context, and displays the page to the user.

Do you remember the opening scene of Severance, when Helly’s innie woke up at Lumon and had to ask a lot of questions to get her bearings? Well, you don’t need to ask many questions, as we’ve got the answers—here’s how you get started with MCP!

How To Get Started With MCP

If you're a developer building an AI app and want it to interact with other apps, MCP is worth exploring. It allows your AI to trigger actions in external systems through a standard protocol.

Let’s take Claude, for example. If you're using Claude for Work, you can already test MCP servers locally by connecting Claude to your internal systems and datasets. Soon, developer toolkits will be available to deploy remote production MCP servers supporting your entire organization.

To start building:

Install pre-built MCP servers through the Claude Desktop app
Follow the guide to build your first MCP server
Contribute to our open-source repositories of connectors and implementations

If you are not using Claude, you don’t have to build your own server either. There are existing MCP servers available, and you can find a list here.

For a deeper dive and step-by-step guidance on implementation, check out the official documentation.

Final Words

MCP is evolving fast, from a concept to a powerful standard that turns AI from a passive observer into an active doer. It makes connecting AI agents with external systems easier and more effective. With each update, MCP becomes more stable and capable. It’s an open, community-driven effort, so keep an eye on the roadmap, share your feedback, and help shape its future.

To dive deeper into the fascinating world of AI technology and discover the latest insights, advancements and innovative applications, click here now!

Frequently Asked Questions

What Is Model Context Protocol (MCP) And Why Does It Matter?

MCP is a communication protocol that gives AI models the context they need to take meaningful actions—like sending messages or triggering workflows—instead of just generating content. It bridges the gap between AI and real-world tools.

How Does MCP Improve The Capabilities Of AI Assistants?

MCP turns AI from a passive responder into an active doer. By connecting to external tools and databases, AI assistants can fetch data, complete tasks, and interact with apps—all using one simplified, standard protocol.

Do I Need To Build My Own MCP Server To Get Started?

Not at all! Developers can use existing MCP servers or install pre-built ones. Whether you're using Claude or another AI tool, you can start testing and building with step-by-step guides and open-source resources available online.

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