What Is MCP (Model Context Protocol)? The Future Standard Connecting AI and External Tools

Introduction

AI technology has come a long way in just a few years. Today, it can generate text, recognize images, and even support decision-making in business. It's quickly becoming a part of our everyday lives and work in ways we couldn’t have imagined before. But for AI to truly reach its full potential, it needs more than just a vast amount of knowledge.

The real game-changer is how well AI can connect with the tools and databases we actually use. Imagine an AI that automatically adds meetings to your calendar, pulls up the information you need from a database, or answers your questions instantly in a chat. If AI could seamlessly work with all these external services, our productivity would skyrocket. To make this happen, we need a "common language" that lets AI and external tools talk to each other smoothly.

This is exactly where Anthropic’s idea of the "MCP (Model Context Protocol)" comes in.

MCP is like a set of traffic rules for how AI and external services communicate safely and flexibly. You can think of it like a "USB-C" for AI—a future where connecting to any service is as simple as plugging in a cable. Plus, MCP is open and highly adaptable, which means anyone can use it and build on it. That’s a huge plus for innovation.

In this article, we’ll dive into what MCP is all about. We’ll break down its structure, show real-world examples, and explore what it could mean for the future. If you’re excited about a world where AI becomes an even more helpful part of daily life, keep reading!

📖 TOC

• What Is MCP? The Open-Source Standard AI and Tool Connectivity
• Why MCP Is Capturing Attention Across the AI Industry
• Can MCP Become the Universal Standard for AI-Tool Communication?

What Is MCP? The Open-Source Standard AI and Tool Connectivity

MCP (Model Context Protocol) is a standard proposed by Anthropic in November 2024 to connect AI models with external tools and services. The word "protocol" might sound a little technical, but it basically just means a set of rules or agreements. In the world of computers and networks, protocols are what allow different devices to communicate smoothly. For example, thanks to protocols, we’re able to send emails or browse the web without a second thought.

So far, AI has mostly been used for things like generating text or answering questions. But that’s starting to change. In the future, AI is expected to handle more real-world tasks—like adding events to your calendar or pulling data from a database—essentially acting like a smart assistant that manages workflows. To make that leap, AI needs a safe, reliable way to interact with outside information and services. That’s exactly what MCP is designed to do. It acts as a common layer that bridges AI models and external tools.

People often describe MCP as the "USB-C for the AI world." In simple terms, it’s aiming to create a future where connecting AI to any service is as easy as plugging in a cable.

What makes MCP even more exciting is that it’s completely open and not tied to any specific company or product. It’s available publicly on GitHub so anyone can jump in, use it, and even contribute to its growth. This openness makes it much easier for different services to stay compatible, paving the way for more flexible and scalable integrations.

Overview of MCP Architecture: The Four Core Components

MCP is built on a client-server model, but it organizes things a little differently compared to a typical web system. Let’s take a closer look at the four main parts that make up MCP.

• MCP Host (Host): This is the application that interacts directly with the user—think of chatbot interfaces, agent UIs, or code editors. The Host has a built-in LLM (Large Language Model) that processes the user’s natural language input. When the model needs to access an external tool, it sends out a request. In short, the Host is where the AI runs and acts as the "bridge" connecting it to outside services.

• MCP Client (Client): This lives inside the Host. Its job is to create a direct, one-on-one connection with the MCP Server. It sends out requests, receives responses, manages sessions, handles errors, and takes care of security. Without the Client, there would be no smooth or safe way for the AI to communicate with external tools.

• MCP Server (Server): This program connects to specific external services and makes their functions available to the MCP Client. For example, a Server might link up with a calendar app, a file storage system, a team chat tool, or a company database. MCP Servers are designed to be lightweight and focused on specific tasks, so they stay fast and efficient.

• Data Sources/Services: These are the actual resources that the MCP Server talks to. They could be anything from local files to SaaS APIs or internal business systems. MCP acts as a middleman, keeping the AI safely separated from directly handling raw data while still giving it the information it needs.

How MCP's Asynchronous, Bidirectional Sessions Unlock Real-Time AI Collaboration

One of the big strengths of MCP is that it supports asynchronous, bidirectional communication. That means it’s not just the client sending requests—the server can also reach out and send messages to the client whenever needed.

At the heart of this system is the "JSON-RPC 2.0" protocol. It’s known for being lightweight and easy to work with. Messages are simple and fall into three types: "requests," "responses," and "notifications." Each message clearly states the method being called and any parameters involved, so it’s easy to understand what action is being requested and what result comes back.

MCP also gives you flexibility when it comes to how messages are transmitted. It supports several communication channels, like Unix sockets, WebSockets, HTTP streams, and more. This way, you can pick the method that best suits your environment or system. When the client and server first connect, they go through a "handshake" to confirm they’re using compatible protocol versions and features.

Another important aspect is that MCP uses "stateful" communication. Instead of treating each interaction like a separate, one-off event, it keeps track of everything throughout a session. This allows tasks to continue smoothly even if something gets interrupted—you can pick up right where you left off. The model can also keep an eye on the status of tools and resources as they change.

On top of that, MCP lets the server send real-time notifications to the client about tool updates, resource changes, or any errors. This creates a much more interactive and flexible way for AI models and external services to work together.

The Three Primitives That Enable AI to Access and Act on External Resources

In MCP, AI models interact with external resources and tools through three basic building blocks, called "primitives" (think of them as simple interfaces). This setup makes it easy to handle a wide range of tasks, while also boosting reusability and scalability.

• Tools: These are like special functions the AI can call when it needs to take action outside itself—such as scheduling a meeting, adding a new entry to a database, or pulling information from an external API. Tools create "side effects," meaning they actually change something in the outside world. Anthropic calls these "action primitives," and they’re considered the model’s core way to proactively get things done.

• Resources: These give the model read-only access to information, like pulling the contents of a file, browsing articles from a knowledge base, or running a database query. Resources don’t cause any changes; they’re just there to supply the model with reliable, detailed material to base its responses on. Think of Resources as the raw ingredients that help the AI stay informed and accurate.

• Prompts: These are preset instructions or templates that guide how the AI responds. They might include things like standard ways to open a summary, formats for answering FAQs, or settings that tweak the tone and style of the output. Using Prompts helps make the AI’s responses more consistent, polished, and user-friendly.

Want to dive deeper into how it all works? Check out the official MCP documentation for more examples and technical details.

How MCP Is Paving the Way for AI to Handle Real-World Tasks

MCP is opening the door for AI to take on real-world tasks by providing a standardized way for AI to communicate and interact with external systems. Up until now, large language models (LLMs) have mainly been about understanding and generating natural language. But MCP moves things forward by laying the groundwork for "agent-type AI"—systems that can seamlessly plug into a variety of external tools.

One of the most exciting parts is that Anthropic has made MCP openly available. This means not just individual developers, but also companies and organizations, can build and run their own MCP-compliant servers and hosts. By keeping MCP open, it’s helping the AI ecosystem grow and evolve much faster, bringing us closer to a future where AI naturally fits into both our work and daily lives.

As more tools and systems start adopting MCP, AI models will be able to connect and interact with the outside world more easily than ever. This will make AI behavior, which often feels like a "black box," more transparent and manageable. With MCP, AI can start to operate more independently while staying aligned with real-world needs—and it’s a major step toward making that future a reality.

---

Why MCP Is Capturing Attention Across the AI Industry

In the world of Large Language Models (LLMs), something called "MCP"—short for Model Context Protocol—is getting a lot of attention lately. And for good reason. MCP offers smart fixes for two major challenges that have been frustrating AI developers for years.

The first big issue is limited knowledge. Traditional AI models can only work with the information they were trained on. If something new pops up—like a fresh internal report or a real-time update—the AI has no way to access it. Without a method for pulling in up-to-date, external data, these systems quickly hit a wall.

The second challenge is information overload. Even if you have a mountain of valuable documents, you can only feed a limited amount into an AI at once. If key pieces are missing, the AI's answers can end up incomplete or off-target. It's like trying to finish a puzzle without all the pieces.

That’s where MCP comes into play. Instead of needing to know everything from the start, AI powered by MCP can fetch the right data as needed. This makes the AI far more flexible, responsive, and useful in real-world situations. That dynamic ability to pull in exactly what's needed is a big part of why MCP is making waves right now—especially for anyone serious about making AI practical for everyday work.

Delivering Fresh, External Information to AI Models

When you start using Large Language Models (LLMs) like GPT-4 or Claude for business or other real-world tasks, one major challenge shows up pretty quickly: they only know what they were trained on. These models are incredibly smart when it comes to general knowledge, but they don’t automatically have access to your company’s specific information or the latest updates from your internal systems.

For example, if you ask, "What are the latest procedures listed on our internal wiki?" the model won’t be able to answer unless that information was part of its original training. That’s because LLMs aren’t naturally hooked up to external sources of information.

This creates a common problem known as "information silos," where important knowledge exists but stays out of the model’s reach. In real-world situations, these silos can be a huge roadblock. You might have a model ready to assist with anything, but if it can’t access your company’s critical data, it’s like trying to work with one hand tied behind your back.

That’s exactly why MCP is getting so much attention right now. It helps close this gap by giving AI the ability to access the information it needs, exactly when it needs it.

Tackling the Context Length Problem: Why Traditional AI Limits Fall Short

Even the most advanced Large Language Models (LLMs) like GPT-4 and Claude have limits on how much information they can handle at once. A typical GPT-4 model can process a few thousand tokens, which is only about a few pages of text. Even the newer, extended versions that can manage around 100,000 tokens still hit their limits pretty quickly.

The reality is that most companies have way more knowledge and documentation than could ever fit into a single prompt. Trying to cram all that information into the model just isn’t practical. So when you’re asking complex questions that involve a lot of data, there’s always a risk the model could miss something important simply because it didn’t have access to all the right pieces.

It gets even trickier when you try to jam more information into the model. The more you add, the slower it processes everything — and if you're using an API, longer processing times can also mean higher costs. That’s why older approaches like “make the model memorize everything” or “stuff all the documents into the prompt” are quickly becoming outdated and unrealistic for real-world use today.

What Is RAG, and Where Does It Fall Short in Enabling Real-World AI Actions?

To tackle the challenges of limited AI knowledge and tight context limits, a method called RAG — short for Retrieval-Augmented Generation — was developed. With RAG, when a user asks a question, the system automatically pulls in the most relevant information and adds it to the AI’s prompt. This way, the AI can answer questions using up-to-date internal company data, even if that information wasn’t part of its original training.

RAG has become especially popular for building things like internal FAQ bots. It’s a smart way to "keep knowledge fresh" without having to constantly retrain the model. (If you’re curious to dive deeper into how RAG works, you might want to check out the article The Future of RAG with AI Agents: How Information Retrieval Is Powering Smarter Actions.)

But RAG isn’t without its limits. Its strength is in "searching, reading, and delivering information" — not actually performing tasks. For example, RAG can find and explain a set of procedures from an internal wiki, but it can’t take that information and create a new task in a ticketing system for you.

If you want AI to not just provide information but also carry out real-world actions, you’ll need something stronger. Tools like OpenAI’s Function Calling or ChatGPT Plugins are built for exactly that kind of work. So if your goal is "delegating work to AI" instead of just "getting answers," RAG alone won’t get you all the way there.

MCP’s Breakthrough: Merging Information Retrieval and Action Execution

TTo fully unlock the potential of Large Language Models (LLMs) in business operations, there are two must-have capabilities.

First, the AI needs to be able to access the external data it needs. Second, it must be able to operate external systems and tools.

Up until now, these two needs have been handled separately. Information retrieval was typically done using methods like RAG, while tool operations were tackled with Function Calling or plugins. But what makes the newly introduced MCP (Model Context Protocol) so exciting is that it brings both of these capabilities together under one framework.

Anthropic has also stressed this point, saying that no matter how advanced a model is, it’s not truly practical unless it’s connected to external systems. That’s why they’re putting such strong emphasis on using MCP to firmly link AI with internal data and business tools.

The result? AI can now deliver real, practical support by understanding much deeper business context. We're moving into an era where AI won't just act like a "smart chatbot"—it will work more like a true partner, deeply embedded in actual business operations.

---

Can MCP Become the Universal Standard for AI-Tool Communication?

MCP, or Model Context Protocol, is starting to get a lot of attention as large language models (LLMs) move beyond experiments and into real-world applications.

As mentioned earlier, MCP offers a framework that makes business use of AI much more practical. It brings information retrieval and tool operation together into one system. While there’s some overlap with technologies like OpenAI’s Function Calling, MCP stands out because it’s broader in scope, more flexible, and more versatile.

For companies, MCP fits perfectly with common needs like connecting to internal knowledge bases and automating tools. It’s especially attractive for enterprise use because it lets organizations keep tight control over security and manage their operating environments more easily.

Across the AI industry, the need for a common protocol that links AI to external data sources is only expected to grow. MCP has a real shot at becoming the industry standard, and it’s already starting to gain momentum among developers and businesses. As more people get behind it, efforts to standardize MCP are likely to speed up.

MCP isn’t just a handy tool—it’s shaping up to be a major force in making AI more practical and usable. It’s well on its way to becoming a key part of how we put AI to work in the future.

Comparing MCP vs. OpenAI’s Function Calling and Plugins

OpenAI offers ways to connect AI to external tools through its Function Calling feature and ChatGPT plugins. While these are useful, they come with some built-in limitations. They’re tied specifically to OpenAI’s models and platforms, which makes it harder to use them with other large language models (LLMs). On top of that, each plugin needs its own OpenAPI definition, meaning the connection methods aren’t fully standardized yet. This leaves room for improvement when it comes to flexibility and versatility.

MCP takes a different approach. It’s designed to be open and vendor-free, making it much more flexible. You can connect it to any model and any tool without being locked into a specific platform. Plus, MCP supports more advanced features like maintaining session states across conversations, combining multiple tools in a single flow, and carefully controlling the order of operations. While OpenAI’s Function Calling is built around a “single-step processing” model, MCP focuses on “continuous and structured interaction,” which allows for more complex and natural workflows.

Among AI experts, MCP is gaining a reputation as a more complete and forward-looking approach. It’s widely expected to become a leading candidate for building the next generation of infrastructure that connects models and tools across the AI industry.

Why Enterprises Are Rushing to Adopt MCP for Secure, Scalable AI

In the enterprise world, there’s a growing push to use AI in ways that are both secure and scalable. In this environment, MCP is getting a lot of attention as a core technology for companies looking to bring large language models (LLMs) into their operations.

Anthropic has helped drive this momentum by open-sourcing MCP and offering an MCP server that comes with built-in connectors for popular enterprise systems like Google Drive, Slack, GitHub, and Postgres. Thanks to these ready-made connectors, companies can quickly link their AI assistants to their own cloud storage, communication tools, and code repositories. This dramatically cuts down on both implementation costs and development time.

One of MCP’s biggest advantages is that it can run in on-premises environments. That means companies can integrate AI without sending sensitive data outside their own secure networks. Anthropic’s enterprise service, "Claude for Work," also supports local MCP server connections, making it even easier for businesses to safely leverage their internal data for AI applications.

With these kinds of advancements, the use of AI in the enterprise sector is growing fast—and MCP looks poised to become a central piece of this expansion.

Building a Unified Future for AI Connectivity

MCP is currently being developed as an open project, led by Anthropic, with the goal of creating a "neutral standard that anyone can use." This approach has earned strong support from developer communities, with many praising it as a big step forward for improving interoperability between systems.

If MCP becomes widely adopted, it could make it much easier to connect LLMs with external tools in a reusable way. That means different products and services would work together more smoothly, potentially speeding up the overall growth of the AI ecosystem.

The CTO of Block has pointed out that "open technologies like MCP will serve as a bridge between AI and real-world applications," and we’re already seeing the open-source community get involved. Improvements from OSS developers and the creation of SDKs are expected to keep pushing the project forward.

That said, for MCP to truly establish itself as an industry standard, it needs adoption from the major players. Right now, big companies like OpenAI and Google are each building their own proprietary systems that aren’t compatible with MCP. Still, there's a clear and growing demand among developers for "standardized tool integration," and MCP is emerging as a strong contender to meet that need.

Because of its flexibility and neutral positioning, MCP also has the potential to become like a "neural network," connecting AI models with information sources around the world.

Looking ahead, MCP adoption is expected to grow, especially in the enterprise space. It could soon become a key piece of infrastructure for making the most out of LLMs. Companies and developers who start exploring MCP integration now might be getting a valuable head start on the next generation of AI innovation.

Epismo is a task management tool that enables seamless collaboration not only with humans but also with AI agents as if they were teammates. It offers a new experience by allowing you to assign tasks to highly advanced AI agents, aiming to solve fundamental challenges in project management and business operations. The basic features are available for free, so feel free to try it out for managing your personal tasks.