Model Context Protocol (MCP)

For readers in a hurry:
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MCP - the "USB port" for AI agents: Find out how the Model Context Protocol (MCP) builds a unified bridge between AI and tools such as CRM systems, cloud storage or APIs.
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Intelligent agents understand your systems: LLMs such as GPT-4 or Claude use external functions independently - without manual intervention.
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From integration chaos to efficiency: Why standardized interfaces via MCP drastically simplify the integration of AI into your business processes.
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Concrete business cases: Experience real examples of how MCP is used successfully in practice.
The rapid development of Large Language Models (LLMs) such as GPT-4 from OpenAI or Claude from Anthropic has revolutionized the way companies and developers interact with AI models. But with the growing variety of tools, data sources and interfaces comes a new challenge: how can AI systems be standardized and efficiently integrated into existing workflows and use these tools? This is precisely where the Model Context Protocol (MCP) comes in.
What is the Model Context Protocol?
The Model Context Protocol (MCP) is an open protocol introduced in introduced by Anthropic in November 2024. It was developed to simplify and standardize communication and interaction between AI agents or LLMs and external tools. It provides standardized access to functions in a way that is understandable and usable for AI agents. MCP is the USB plug for AI agents to their tools and data sources.
Instead of programming individual interfaces for each system, MCP provides a standardized way of defining functions as so-called capabilities capabilities. These are managed by an MCP server or service via which the client and server communicate.
How AI agents communicate via MCP
A central concept is functional abstraction: systems such as a CRM or cloud storage (e.g. Google Drive) provide individual functions via MCP - for example:
- Function 1: Show me the last orders and details for customer Max Muster.
- Function 2: Show me all after-sales conversations with the customer Max Muster for order number "0321456".
An AI assistant uses these functions independently in an agent-based workflow. It transfers relevant parameters such as customer names or order numbers to the MCP server because, thanks to the MCP standard, it knows and understands what capabilities the MCP server has and what parameters it requires to execute them. The server processes the request and performs authentication if necessary. It then retrieves the data via corresponding APIs and delivers a structured response. The language model then prepares the information in a way that the user can understand. However, the complexity of the APIs themselves remains hidden from the AI agent; the MCP server abstracts these and translates them into the MCP standard.
In this way, MCP ensures that even complex systems can be used in an AI-compatible manner - regardless of whether it is a CRM, cloud storage, internal databases or user-defined AI tools.
How does MCP work?
The key challenge for AI systems is to access essential data that is distributed across numerous platforms and systems or integrated in outdated technologies. The Model Context Protocol (MCP) was developed precisely to address this problem.
At the core of this solution is an MCP server acts as a central interface that mediates between AI agents, data sources and APIs - similar to a USB Type-C port that connects different devices with a single cable. MCP provides a repository with clearly defined endpoints through which AI models can seamlessly access required functions.

Example workflow:
- A user requests a AI assistants in MS Teams or Slack, the last open pull requests from a GitHub Repository to analyze.
- The agent formulates an internal Promptby requesting suitable functions via MCP.
- The MCP server checks Authorizationsauthenticates the Server accessretrieves the data and sends a structured Response back to the AI assistant.
- The AI model evaluates the data and presents it to the user.
The decisive factor is that the agent understands which capabilities are available, what they do and how they can be addressed, as MCP clearly specifies these functions.
You can find more information on the topic of AI agents in our blog article AI Agents - Intelligent helpers. Find out how AI agents take on tasks independently, make decisions and support companies in their digital transformation.
MCP in practice: A concrete use case
A company would like to offer an AI agent to automate the preparation of a customer appointment. The agent should do this:
- Retrieve a customer's order data,
- Evaluate after-sales communication,
- relevant documents from Google Drive add.
Via a own MCP server the company can provide this functionality. Each task is a clearly defined capability. Based on the description, the AI agent recognizes which function is useful and when, calls it up, receives the data and prepares it in a user-friendly way.
ResultThe workflow remains consistent, secure and reusable - a clear strength of a clear strength of MCP in real business scenarios.
It should be emphasized that the user does not have to tell the AI agent which MCP skills to call up. Instead, the user simply informs the AI agent of their intention or goal to prepare the customer appointment in the best possible way, while the AI agent independently selects and calls up the necessary MCP skills and then presents the result to the user in a structured manner.
Who is MCP relevant for?
- developerwho AI applications with various external tools tools.
- AI companiesthat want to modularize their solutions and make them easier to integrate.
- Providers of APIs that AI tools and AI agents want to make them directly usable.
- Companies looking for secure and flexible ways to retrieve datawithout having to customize their systems.
MCP supports various programming languages via software development kits (SDKs)which also allows implementations into existing systems is also made easier. This reduces complexity - and creates scope for creative use cases. use cases.
The future of MCP
The future of MCP As AI agents increasingly act autonomously, collect information and communicate with digital networks, a reliable protocol is needed to enable this interaction. MCP would be not just a technical standard, but a strategic lever for innovation.
Whether in start-ups, enterprise software or the public sector - anyone who AI-based workflows want to implement AI-based workflows efficiently MCP can hardly be ignored.
However, MCP is not absolutely necessary in every scenario. In some cases, specialized API integrations or established middleware solutions are sufficient to ensure effective communication between systems. Ultimately, the specific use case determines which protocol offers the best added value.
Conclusion
Although MCPs are not absolutely necessary for integrating tools into LLM applications, they do offer decisive advantages:
- Standardized interfaces,
- lower development costs,
- greater flexibility and scalability.
Especially for complex applications with many data sources, MCPs enable consistent integration - a real added value for companies that want to professionalize their AI processes.
If you want to contribute to the next generation of AI applications, you should familiarize yourself with the Model Context Protocol now - and discover what is possible with a well thought-out approach.
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About Business Automatica GmbH:
Business Automatica reduces process costs by automating manual activities, increases the quality of data exchange in complex system architectures and connects on-premise systems with modern cloud and SaaS architectures. Applied artificial intelligence in the company is an integral part of this. Business Automatica also offers automation solutions from the cloud that are geared towards cyber security.
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