Hello!

Today we take a close look at MCP, one of the most talked-about technologies right now!

Introduction

"I get that AI is useful, but letting it access my own data or work with other applications sounds difficult..."

Many people probably share this concern.

Indeed, conventional AI faced major walls: it could only answer within the scope of its training data, could not access real-time information, and could not operate external applications.

Since the early days of LLMs, a technique known as RAG has evolved as a way to effectively search data that lies outside the training data.

Beyond data retrieval, a technology has now emerged that lets AI connect with tools across virtually every domain in a semi-automatic way.

That technology is the "Model Context Protocol (MCP)."

In this article, we explain MCP — which makes connecting AI to external tools dramatically simpler — from the basics through practical use.

The Essence of MCP: A Standard Interface for AI

MCP is a protocol that standardizes communication between AI models and external tools and applications. In the world of the internet, it is comparable to HTTP or SMTP — a common language for connecting diverse systems.

There is an important difference, however. Whereas HTTP and SMTP are protocols designed primarily for human developers to understand and implement, MCP is designed as a protocol for the "AI itself" to understand and use.

In other words, MCP is not an "API for humans" but an "API for AI."

Traditionally, adding external capabilities to an AI required a separate implementation for each tool or API. That might be manageable for a small project, but when integrating many tools, development and maintenance became extremely complex.

Enter MCP.

Because you can communicate with every tool in a unified way, developers no longer need to learn a different integration method for each tool, and the AI can understand how to use tools in a consistent manner.

The Concrete Value of MCP: An Everyday Example

To understand the value of MCP, let's consider an everyday example.

Suppose you ask an AI assistant to help plan a trip.

"I'd like to plan a trip to Kyoto next weekend"

Let's consider this simple request.

Without MCP, the AI would give you general tourist information about Kyoto.

"Kyoto has famous sights such as Kinkaku-ji and Kiyomizu-dera. Cherry blossoms are beautiful in spring, and autumn leaves in fall."

To be fair, the latest LLMs are not quite that clueless, but this answer clearly lacks specifics — and it is not tailored to you.

With MCP, the AI can connect to multiple external services and provide information such as

• Next weekend's weather forecast for Kyoto (real-time meteorological data)
• Hotels currently available for booking, with rates (data from travel booking sites)
• Special events being held that weekend (the latest information from tourism sites)
• Transit service status and congestion forecasts (data from transportation information services)

In this way, an AI that uses MCP goes beyond "telling you what it knows" to "fetching the information it needs before telling you."

At this point you might think, "Isn't that just plain API integration?"

Exactly — existing LLMs already had a "tool use" capability that made API integration possible. What MCP does is standardize the way that API integration is done.

How MCP Works: The Internal Mechanism

The internal mechanism of MCP is surprisingly simple

1. Self-describing tool definitions
   Each tool describes its capabilities, required parameters, and the format of the information it returns, all in a standardized format — typically expressed as JSON or similar.
   
2. A standardized communication protocol
   The exchange between the AI and tools is standardized, with clearly defined ways to send requests, receive responses, and handle errors.
   
3. A tool discovery mechanism
   The AI can dynamically retrieve, from the MCP server it connects to, a list of available tools and how to use each one.

Thanks to these elements, the AI can "understand" and use tools without a human explaining the protocol to it.

For example, an AI connected to a hotel-booking MCP tool automatically learns from the tool that it offers functions such as "search hotels" and "make a reservation," along with the parameters those functions require (location, check-in date, check-out date, and so on).

How MCP Differs from SOAP and JSON Schema: A Protocol for AI

When first encountering MCP, experienced developers in particular may think, "Isn't this just like SOAP or JSON Schema?"

It is true that MCP is a JSON-based protocol with conceptual similarities to SOAP and REST, but there is a fundamental difference.

SOAP and JSON Schema were designed primarily so that "human developers" could understand and implement APIs.
Developers read the documentation, understand it, and write code that uses the interface.

MCP, by contrast, is designed so that the "AI model itself" can understand and use the interface. It includes metadata and description formats that allow the AI to grasp a tool's purpose, capabilities, and usage.Human developers do not need to explain in detail how to use the API — that is the innovative part of MCP.

For example, with SOAP, a developer reads a WSDL file to understand how to use an API (though tools like Axis did act as WSDL translators). With MCP, the AI itself "reads" the tool descriptions provided by the server and understands how to use them. This means AI can discover, learn, and use tools without human intervention.

In this respect, MCP moves close to the ideals of the "Semantic Web" once championed years ago.

That said — the Semantic Web — does anyone still remember it?

(Please don't call us internet old-timers)

The Semantic Web was a vision of making web content "understandable" not only to humans but to computers as well.

For many years it was considered hard to realize, but combined with the capabilities of large language models, MCP is arguably bringing part of that vision to life. AI understands the meaning (semantics) of tools and uses them appropriately — in that sense, MCP may well be a modern realization of the Semantic Web!

How MCP Differs from Conventional Tool Integration: What Makes It Innovative

At the risk of some repetition, this point matters — so to appreciate MCP's innovation further, let's compare it with conventional tool integration approaches.

Integration method

Conventional approach
Each tool required its own implementation, and integration methods varied by framework and platform. Developers had to learn how to integrate each tool individually.

MCP approach
With a standardized protocol, every tool integrates the same way. Since any tool can be invoked through the same interface, once you understand the MCP concept, adding new tools becomes easy.

Manageability

Conventional approach
The more tools you added, the more complex management became; authentication and error handling had to be implemented per tool. Integrating many tools tended to bloat the codebase.

MCP approach
Tools can be managed in a central MCP registry, with standardized authentication and security. Even with hundreds of tools added, the architecture stays the same, giving excellent scalability.

Code comparison

With the conventional approach, each tool required a different implementation

# Conventional approach (illustrative)
from weather_api import WeatherAPI
from hotel_booking import HotelBooking
from flight_search import FlightSearch

weather_client = WeatherAPI(api_key="...")
hotel_client = HotelBooking(username="...", password="...")
flight_client = FlightSearch(api_key="...")

# Each tool has a different interface
weather = weather_client.get_forecast("Kyoto", days=3)
hotels = hotel_client.search_rooms(location="Kyoto", checkin="2023-06-10")
flights = flight_client.find_flights(origin="Tokyo", destination="Kyoto")

The MCP approach, by contrast, uses a unified interface

# MCP approach (illustrative)
from mcp import MCPClient

# Access every tool in a unified way
mcp_client = MCPClient()
weather = mcp_client.call_tool("weather", {"location": "Kyoto", "days": 3})
hotels = mcp_client.call_tool("hotel_booking", {"location": "Kyoto", "checkin": "2023-06-10"})
flights = mcp_client.call_tool("flight_search", {"origin": "Tokyo", "destination": "Kyoto"})

Because MCP lets you handle multiple tools with the same pattern, the code stays simple and consistent.

MCP and RAG: Two Complementary Technologies

When discussing MCP, it also helps to understand its relationship with RAG (Retrieval-Augmented Generation).

RAG is the process by which an AI searches external knowledge bases (documents, databases, and so on) and uses what it finds to generate answers. MCP, on the other hand, is a standard protocol for AI to communicate with external tools, APIs, and applications.

The two serve different purposes but work in a complementary way

• RAG focuses on "what" to retrieve, and excels at searching and using static data.
  
• MCP provides a standard for "how" to connect with external systems, enabling dynamic tool use and interaction.

In real AI applications, combining RAG and MCP allows you to build more flexible and powerful systems. For example, depending on the user's question, you might search documents with RAG and, when needed, call external tools via MCP.

AI Capability and MCP: Not Every AI Can Use It

To use MCP effectively, the AI model itself must have a certain level of capability. Not every AI model can make full use of MCP.

The capabilities an AI needs in order to use MCP include:

1. Natural language understanding
   The ability to understand the user's request and decide which tool to use
   
2. Tool-use capability
   The ability to understand a tool's description and invoke it with the correct parameters
   
3. Result interpretation
   The ability to understand the tool's response and explain it to the user

Smaller language models (on the order of a few billion parameters) often fall short in these capabilities and may not fully benefit from MCP. They may, for example, pick the wrong tool or fail to construct parameter formats correctly.

In practice, it is advisable to use MCP with models of sufficient scale and capability (tens of billions of parameters or more). The latest large language models are already capable of using MCP effectively.

MCP in Practice: From the User's Perspective

Let's look at how MCP works from a user's point of view.

Suppose you want to connect your company's database to an AI chatbot. Previously, this required developing a custom API and doing complex integration work with the AI.

With MCP, the flow looks like this:

1. Set up an MCP server for the database (or use an existing one)
2. Configure the necessary authentication
3. Enable MCP integration on your AI platform and register the server's details

With just that, the AI "understands" the database's structure and how to operate it, and can access the database appropriately in response to user questions.

The key point is that humans do not need to explain, in detail, the protocol by which the AI "understands" how to use the database. Because the MCP server itself provides self-describing information, the AI learns how to use the tool automatically.

From a security standpoint, however, authentication and permission settings must be configured carefully. As convenient as MCP is, improper configuration can open the door to unauthorized access to your data.

The Flexibility of MCP: Why It Is So Broadly Applicable

The reason MCP can be used in so many situations lies in its flexible design.

1. A protocol-based design philosophy
   Because it is designed as a "protocol" rather than a specific implementation, it is language- and framework-agnostic and can be used across diverse systems.
   
2. Support for multiple connection methods
   It supports both HTTP-based connections (SSE) and local command execution (STDIO), covering everything from cloud services to local tools.
   
3. Dynamic tool discovery
   The AI can discover available tools at runtime, so it does not depend on a hardcoded list of tools prepared in advance.
   
4. Separation of authentication and permissions
   It supports different authentication methods per tool and per-user access permissions, enabling flexible integration while maintaining security.

These characteristics make MCP a general-purpose protocol that is not limited to a single use case and can be applied in a wide variety of settings.

Real-World Uses of MCP: Learning from Concrete Examples

To understand the value of MCP more concretely, let's look at a few usage scenarios.

Access to corporate knowledge bases

Companies hold vast internal documents, reports, and manuals. With MCP, an AI can access these knowledge bases and answer employees' questions accurately.

Questions like "What is the latest status of Project X?" or "How do I troubleshoot Product Y?" can be answered by pulling the latest information from internal systems.

Developer support tools

For software developers, MCP is a powerful ally. The AI can connect with Git repositories, codebases, and build systems to answer developers' questions and suggest code improvements.

Questions like "What is causing this bug?" or "How do I use this API function?" can be answered while referencing the actual codebase.

Personal assistants

For individual users too, MCP makes daily life more convenient. An AI connected to calendars, email, and task management tools can smoothly support the user's day-to-day.

Requests like "What's on my schedule next week?" or "Notify me if an important email arrives" can be handled properly by connecting to the user's personal accounts.

MCP Security and Ethics: Caveats and Considerations

While taking advantage of MCP's powerful capabilities, we also need to consider security and ethical aspects.

Security considerations

Because MCP grants AI access to external systems, proper security measures are essential:

• Authentication and permission management: fine-grained permissions per user and per tool
• Data protection: policies for handling sensitive information
• Auditing and monitoring: logging and monitoring of the AI's tool usage

Ethical considerations

Granting AI greater capabilities also raises ethical questions that should be addressed:

• Transparency: ensure users can understand and control the AI's tool usage
• Consent: do not use tools without the user's explicit permission
• Human oversight: require human confirmation for important decisions and transactions

By properly addressing these considerations, you can maximize the benefits of MCP while keeping the risks to a minimum.

Conclusion: The Future of AI with MCP — and the Semantic Web Realized

MCP is an innovative technology that greatly expands what AI can do by standardizing how AI connects to external tools. Tool integrations that once had to be implemented individually can now be achieved easily through a unified protocol.

What deserves special mention is that MCP is designed as a "protocol for AI." Just as human developers read API documentation, the AI itself can "understand" a tool's capabilities and usage through the protocol. This evolves AI from a mere information provider into an active agent that can actually carry out tasks.

Now, at the risk of repeating ourselves,
looking back, there was the "Semantic Web" vision that Tim Berners-Lee proposed in the early 2000s. The idea was to make web content understandable to machines as well as humans, but many barriers stood in the way of realizing it.
Partly in the Semantic Web context, SOAP was developed, WSDL appeared, the term "SOA (service-oriented architecture)" was everywhere, and "Web Services" emerged (not in the sense of services on the web, but as a technology field of its own) — the dream from twenty-some years ago of discovering services via UDDI and integrating them is now becoming reality thanks to intelligent AI.

With the arrival of MCP, that vision is bearing fruit in a modern form. The "machine-readable web" that the Semantic Web aimed for is being realized, transformed, as "AI-readable tools" through MCP.

By allowing AI to understand and use tools without detailed human explanation, MCP opens new possibilities for human-AI collaboration. Tool standardization frees developers from complex integration work so they can focus on more creative aspects. As diverse services and applications connect through MCP, interoperability across the entire AI ecosystem improves. And above all, when AI can take real actions rather than merely answering questions, the user experience changes fundamentally.

The ideal of a "web that understands meaning," dreamed of by the pioneers of the Semantic Web, is being given new life in the world of AI through MCP.

This piece drifted from technical explanation into internet nostalgia along the way — thank you for staying with us through this long read.

In any case, MCP is a genuinely useful technology that has significantly reduced our development effort, and we want to emphasize once more that it is a must-use!