[EN] Einführung in MCP Server

Julian | Sep 12, 2025 min read
[EN] Einführung in MCP Server

MCP Server: How your AI agent can understand the real world

Hello everyone,

There’s simply no getting around the topic of AI. If you delve deeper into the topic, you’ll quickly come across terms like ‘agents’ and ‘MCP servers’ and today I’ll briefly shed light on what the latter is all about.

In the world of artificial intelligence, we face a fundamental challenge: language models are incredibly good at recognizing patterns and generating text, but they live in a static world. You do not have direct access to real-time data, external tools or our personal systems. Imagine if a brilliant consultant could give you advice, but couldn’t send emails or look at your calendar.

This is exactly where the MCP Server comes into play. It is the key that allows AI agents to connect to the real world. The Model Context Protocol (MCP), a new open standard, acts as a bridge that allows AI systems to interact with external data sources and tools in a secure and standardized manner.

What is an MCP server?

A MCP Server is basically a program that acts as a “smart adapter” for your tools and data. Instead of you having to write a separate, complex integration for every API you want an AI agent to use, MCP Server replaces all of these point solutions with a single, unified protocol. Imagine it’s like a USB-C port for AI applications - a standardized way to connect different devices.

An MCP server provides three main types of capabilities to the AI agent:

  • Tools: Functions that can be called by the language model. Examples are sendEmail or get_weather. The AI ​​simply asks for functionality without knowing the details of the API implementation.
  • Resources: Data sources such as files, documents or database queries. An AI agent can access a local file through an MCP server or perform a database query to obtain real-time data.
  • Prompts: Predefined templates that help the agent perform specific tasks.

The Architecture: How it Works

Communication in the MCP network follows a simple client-server model. The AI Agent is the client that sends requests over a secure connection (e.g. over the Internet or locally). The MCP Server receives these requests, carries out the desired action (e.g. makes an API call or reads a file) and sends back the result.

A big advantage of this model is asynchronous communication. The server can send messages to the client at any time as soon as new data is available, which is particularly advantageous in real-time applications.

The benefit for developers

The MCP server brings massive advantages for us developers:

  • Reduced Complexity: Instead of managing N × M integrations (each agent with each API), MCP reduces complexity to N + M (agents and servers connect to the protocol). This saves us a lot of development time and reduces maintenance effort.
  • Enhanced Security: The protocol is designed from the ground up for security and access control. Since the agent does not access the APIs directly, but only via the server, you have an additional level of control.
  • Better Scalability: Standardization allows us to add new tools without changing the code of our AI agent or our backend.

A look into practice

A concrete example of the power of MCP is the GitHub MCP Server, which connects AI tools directly to the GitHub platform. This allows an AI agent to perform complex tasks in “natural language” without knowing the API details.

The server makes it possible to perform the following actions directly via the AI agent:

  • Repository Management: Search repositories for code files, analyze commits, and understand project structure.
  • Issue & PR Automation: Create, edit or manage issues and pull requests. AI can help triage bugs or review code changes.
  • Team Collaboration: Access discussions, manage notifications, and optimize team processes.

This means that a developer can soon say: “Hey Copilot, find all open bugs in our login service and write a pull request to fix them.” The GitHub MCP Server translates this request into the necessary actions within the GitHub ecosystem.

Conclusion

An MCP server turns a “smart” AI system into a “useful” one. It is the bridge that frees AI models from their isolated context and gives them access to real-time data and real-world actions. For anyone at the interface between AI and applications, understanding the Model Context Protocol is essential. It is a game changer that will fundamentally change the way we build AI systems.

Have you already had experience with MCP servers or do you see other use cases? Let me know!