What is an MCP Server?
The abbreviation MCP stands for Model Context Protocol. Behind this technical-sounding term lies an idea that is surprisingly simple and yet extremely effective: an MCP server acts as an intermediary. It connects AI applications, such as specialized agents or chatbots, with existing enterprise systems, including CRM tools, accounting programs, knowledge databases, and ERP systems.
MCP Integration
MCP is often compared to “USB-C for AI”: it represents a standardized bridge that allows an AI model to connect “plug-ins” or services. A connection between existing business logic and the world of modern AI. It ensures that requests, information, and processes end up exactly where they are needed, no matter how complex a company's internal structure may be.
What makes it special is that it takes communication with internal systems to a new, more human level. What used to be possible only with technical workarounds, complex code, or a deep understanding of the system is now accessible to everyone thanks to MCP servers. Instead of thinking in terms of APIs or SQL statements, users can now simply formulate what they want to know or do – the MCP server does the rest. This not only saves time, but also significantly lowers the barrier to entry for smart automation in the company. Without MCP servers, separate connectors must be written for each combination of AI host and target system – a so-called “N×M integration problem.” With MCP, one integration per side (N + M) is sufficient, which drastically reduces the effort required. The MCP ecosystem generally consists of three roles. It connects applications (hosts/clients) with resource providers (servers) via standardized protocols such as JSON-RPC 2.0, usually via HTTP or stream-capable connections. MCP servers can run locally or remotely, while MCP Bridge acts as a proxy, bringing multiple servers together and providing a secure, unified interface.
MCP Server in Live Testing
To demonstrate how flexible and practical MCP Servers can be in practice, here's a concrete example from everyday testing. In a few steps, it becomes clear how content can be completely controlled via the MCP Server—from creation to usage to deletion.
In the first step, an AI agent is asked if there's an article about ICE train types from Deutsche Bahn in the internal knowledge base (see Screenshot 1). The answer is clear: no corresponding entry exists.
Then, an offer is made to automatically generate such an article, including an overview of ICE 1 to ICE T, and insert it directly into the knowledge base (which is accepted). What would normally be a task for editors or developers happens here within seconds via a dialogue interface—made possible by the MCP Server.
Subsequently, the content is tested in a live query: The AI agent was now able to immediately access the new article and provide a complete, structured answer (see Screenshot 2). The content is thus available - without additional manual approval processes.
The process doesn't have to end here. In the next step, the article is deleted again through the same interface—also controlled via the MCP (see Screenshot 3).
A subsequent query shows: The article has indeed disappeared, and the agent can no longer provide any information.
MCP Servers thus enable content and systems to be centrally controlled in real-time—from creation to provisioning to deletion. Everything runs via a unified infrastructure, without requiring additional interfaces or manual intermediate steps.
What previously would have required multiple departments or tools can now be implemented directly via an AI request—quickly, traceable, and scalable for various application areas.
This makes working with knowledge and processes not only more efficient but also more accessible for teams without specialized technical knowledge.
Why are MCP Servers Relevant for Companies?
The example shown clearly demonstrates how flexibly information can be managed via an MCP Server—regardless of whether it has just been created, is actively being used, or needs to be removed. This very ability to centrally connect and control various systems and content is a decisive advantage for companies.
Many organizations work with a variety of tools, systems, and interfaces. Often, each works well individually—but they're not interconnected. This is precisely where the MCP Server comes in. It creates connections where silos previously existed. And this has practical advantages:
- Efficiency gains: When an AI agent wants to answer a customer question, it needs access to data—whether from the ticketing system, online shop, or knowledge database. The MCP Server provides this data—without the need for manual searching.
- Scalability: The same connection that is used for one application today can be used for ten others tomorrow. MCP Servers work modularly—and grow with the requirements.
- Data sovereignty: Especially in regulated industries, the question of data control plays a major role. With an MCP Server, companies retain control over their data flows—and decide for themselves who has access to what.
MCP in the AI Era: Developments in 2025
The hype surrounding AI is real – yet its practical benefits always depend on how well AI is embedded in the company. This is precisely where the MCP server becomes a crucial component. After all, even the best models are of little use if they are unaware of the relevant contexts.
MCP and OpenAI: Latest Developments with ChatGPT
By 2025, OpenAI has gradually integrated the Model Context Protocol (MCP) into its most important products, massively expanding the capabilities of ChatGPT and related tools. This development is particularly visible in three areas:
- Responses API
- Agents SDK and
- Developer Mode of ChatGPT
The Responses API allows remote MCP servers to be integrated directly into a running session. As a result, a model no longer just generates text, but actively queries data from external sources. In addition, specific actions can be triggered in third-party systems, such as starting an automation workflow. This capability makes the API a key tool for developers.
The Agents SDK goes one step further: it enables developers to set up their own MCP servers or reuse existing ones. Various transport mechanisms are supported – from HTTP to Server-Sent Events (SSE) to Streamable HTTP. This allows for the development of customized integrations that can be scaled from small projects to complex enterprise environments.
MCP connectors in ChatGPT Developer Mode have been in use since September 2025. Users can add their own connectors there by simply entering a server URL and configuring the necessary authentication. This allows ChatGPT to perform write actions, such as changing content in internal tools. Currently, however, this only works in the web version. In addition, many connectors are still classified as “unverified.” This means that they have not been officially tested and should only be used with extreme caution.
An important innovation since summer 2025 is Agent Mode. With the help of MCP, this mode allows multi-step workflows to be implemented that link several tools together. An agent can therefore retrieve information from a data source, interpret it, automatically generate a document, and then store it in a company system. Conditional logic and sequencing offer flexible handling depending on results or events, rather than purely linear control of processes.
MCP forms the basis for the transformation of GPTs from a pure language model to an intelligent, active player in the digital ecosystem.
Opportunities and Risks of MCP Servers
The use of MCP servers offers numerous opportunities, the most important of which is that AI systems no longer just respond to questions, but can act actively themselves. Instead of pure interaction, this creates a powerful actor that automates processes and initiates workflows. Uniform interfaces facilitate development and integration into corporate environments is significantly easier. However, MCP also entails serious risks.
“Powerful but dangerous, and intended for developers who understand how to safely configure and test connectors.”
Source: Open AI Platform, 2025
Manipulated inputs — known as prompt injections — can trigger unwanted actions, while compromised MCP servers could steal sensitive data or perform malicious operations. Commands in which faulty or malicious prompts alter or even delete data are particularly sensitive. Added to this is the challenge of complex rights management, which is absolutely essential. Studies such as MCPSecBench 2025 show that common MCP implementations still have significant security gaps: in over 85 percent of the attacks tested, vulnerabilities were successfully exploited. It is therefore always recommended to use security frameworks such as MCP Guardian and to exclusively use trusted/official servers. The successful use of MCP therefore depends on how innovation is handled, but also on the appropriate security precautions.
Outlook: What's Next?
In 2025, it is becoming increasingly clear that the digital infrastructure of the future will not be monolithic, but highly hybrid and modular. Companies are relying on cloud solutions, but are keeping business-critical systems and databases in-house. MCP servers are the connecting element here: they allow specialized AI applications to be seamlessly combined with existing tools and workflows and to respond flexibly to new requirements, channels, or regulatory requirements.
Particularly exciting: MCP marketplaces and connector libraries. Here, companies and developers can obtain tested modules, similar to app stores, and integrate them into their systems immediately. At the same time, low-code and no-code solutions are becoming increasingly important. Visual interfaces make it possible to design data flows and automations using drag-and-drop, without the need for in-depth technical knowledge. MCP servers form the basis here as an interface that reliably and securely translates these configurations into practice. Significant progress will be seen in the area of security and governance: Certified “Verified Connectors” and frameworks such as MCP Guardian aim to build trust and improve deployment in a corporate context. In the long term, standards for role and rights management, logging, and quotas are also likely to become established, making MCP a stable foundation for digital sovereignty.
MCP in use at moinAI
MCP has evolved from a technical concept to a central building block in modern AI systems and this infrastructure also plays a central role at moinAI and the AI chatbot. After all, intelligent AI is only as good as the data it can access and the processes it is integrated into. That is why moinAI also uses MCP servers. The MCP server is a specific implementation that enables AI agents to connect seamlessly to the moinAI knowledge database, facilitating interaction with corporate knowledge. The server allows direct access to content and dynamic integration of knowledge into workflows. Documentation and further information can be found here on Github.
Had enough theory? Then test the moinAI chatbot prototype now and discover the automation potential of your website.
