MCP explained simply: What is the Model Context Protocol and why is it important?

MCP, the Model Context Protocol, is currently a hot topic in the AI world. However, many people still don't know exactly what this term means. In this article, we'll explain what MCP is, why it's so important and what it means for the future of AI-based applications - in an understandable way, even if you're not technically savvy.

What exactly are MCPs and why is everyone talking about them?

MCP stands for Model Context Protocol and is basically a new technical standard that makes it possible to easily connect AI models (such as ChatGPT & Co.) with external services and tools. Sounds technical? Don't worry, it will become clearer in a moment.

The basis: Why technical standards are important

In the Software development developers love standards. They enable smooth communication between different systems. Perhaps you have already heard the term "REST API"This is a typical standard that makes it possible to connect different applications with each other. MCP pursues a similar goal, only specifically tailored to AI systems (so-called "Large Language Models", LLMs for short).

Why do we need MCP at all? What are the LLMs missing?

LLMs, such as ChatGPTare quite limited in themselves. They are great at analysing and formulating texts, but cannot perform any actions on their own. For example, if you ask a chatbot to send an email or create a spreadsheet, it won't be able to do anything on its own - it has no practical connection to the outside world.

The next step: connecting LLMs with tools - and the current problem with this

Developers have now found out how to combine LLMs with external tools, for example with an Internet search function. Services such as ChatGPT or Perplexity rely on external sources to provide you with the latest information. However, each connection to a tool is currently individual and complex:

• Each tool speaks its own language (each API looks different).
• The integration of multiple tools quickly becomes chaotic, complex and error-prone.
• Changes to just one service can throw the entire system into chaos.

This is precisely why we don't yet have an intelligent assistant on the level of Iron Man's "Jarvis".

MCP: The standardised language between AI and tools

This is where MCP comes into play: Imagine you have three tools - one speaks English, one speaks Spanish and one speaks Japanese. MCP acts like an interpreter, translating all languages into a standardised language. This creates smooth communication between the AI and the tools.

MCP is therefore a kind of intermediate level (a technical interface) that makes it much easier to connect external services to AI models. Instead of laboriously establishing individual connections, a single standard is now sufficient.

How MCP works in practice

The MCP ecosystem roughly consists of three parts:

MCP client: Applications such as Tempo or Cursor, which interact with the user and access LLMs directly.

MCP protocol: The "common language" that connects client and server.

MCP server: Is created by the respective tool provider and translates the "language" of the external services into MCP format.

The ingenious thing about this is that providers of external services (e.g. databases, search engines, news services) are themselves responsible for making their service MCP-compatible. In the long term, this means less work for developers building AI-based applications and, at the same time, more flexibility for companies offering services.

What does MCP mean for the future? - Opportunities and challenges

MCP is very promising as it significantly reduces complexity and greatly simplifies the development of AI-based applications. In the long term, this could lead to significantly more intelligent and versatile AI assistants.

However, there are currently still technical hurdles:

• Implementation is currently still relatively complex.
• The standard is not yet fully developed; changes and improvements are likely.

Start-up potential around MCP

Every technological standard also opens up new business opportunities. Some possible ideas:

MCP App Store: A platform on which users can easily select MCP servers and install them in their applications with just a few clicks.

Services for MCP integration: support companies in making their services MCP-compatible.

However, MCP is currently still in its infancy. Those who get involved early on and monitor the market will be able to react quickly and position themselves later on.

Conclusion: MCP - a standard that makes AI smarter

MCP is not "rocket science". It is a simple but powerful technical standard that could revolutionise the connection of AI systems with external tools. We are still at an early stage, but the potential is enormous - both for developers and for companies.

Those who stay on the ball now will not only gain a better understanding of the future of AI systems, but will also secure interesting long-term opportunities.