The problem: AI has no built-in knowledge of internal systems
Language models can analyse text and answer questions. They cannot automatically access current enterprise data or the functions of internal applications. Information from an ERP system, a document repository or a ticketing system often has to be provided by hand. If several AI applications need to work with the same systems, organisations frequently end up with one-off integrations. Those are expensive to build and maintain.
The Model Context Protocol, or MCP, addresses this gap. It defines a uniform way for AI applications to connect to external data sources and functions. Anthropic introduced MCP in 2024 as an open standard. (Anthropic)
The solution: a standardised connection layer
An MCP server exposes a system’s data and functions through the MCP protocol. It can, for example, forward search queries to a document repository, retrieve information from a database, or execute an action in a business system.
The AI application connects to the server via an MCP client. It can discover the available functions and – depending on the task and the configured permissions – call the appropriate ones. The server executes the call and returns the result. The model then uses that result for its answer or the next step. (OpenAI)
Simplified, the flow looks like this:
User → AI application → MCP client → MCP server → enterprise system
Roles matter here: the AI model itself is usually not connected directly to the enterprise system. The AI application acts as the host and controls the MCP connection. The MCP server translates between the standardised protocol and the target system.
What an MCP server can provide
An MCP server can offer three main kinds of capability:
- Tools are callable functions. Examples include “search invoice”, “retrieve customer data” or “create ticket”.
- Resources supply content or data as context, such as documents, records or other information.
- Prompts are reusable templates for specific tasks or dialogues.
For a tool, the AI application typically knows the name, the description and the expected input parameters. That lets the model recognise what the function is for and which details it must supply for a call. (OpenAI)
Example: searching business documents with intent
A company wants employees to ask questions about internal contracts and policies in natural language. Without an integration, someone would first have to find the relevant documents and paste them into the AI application.
With an MCP server, the AI application could instead use a tool such as dokument_suchen. The request “Find the current travel expense policy” is passed to the server. The server validates the request, searches the connected repository and returns matching documents or excerpts. The model then formulates an answer and can cite the sources it found.
The same pattern applies to business systems. An MCP server could expose approved SAP functions, for example to look up a business partner or check the status of a process. Write actions – such as changing a record – should be secured separately.
What MCP does – and what it does not
MCP standardises communication between AI applications and connected servers. It is neither an AI model nor an enterprise system. The MCP server provides a clearly described interface to a system; the actual search or business action is performed by the connected system or the server logic.
The benefit: applications can be built against a shared interface instead of a separate connection for every AI application and every target system. MCP does not, however, remove all integration work. A suitable server still has to be created, configured or connected for each target system. Permissions, data quality and business rules still need to be defined in the organisation. Anthropic describes this approach as a standardised connection between AI assistants and the systems that hold data and functions. (Anthropic)
Security and operations
An MCP server can expose sensitive data or trigger changes in systems. The integration therefore needs the same security requirements as other interfaces:
- Expose only the functions and data that are required.
- Bind permissions to users and roles.
- Validate inputs on the server and log access.
- Restrict write actions and require confirmation where needed.
- Store and transmit credentials securely.
- Run the server only over protected connections and with appropriate authentication.
MCP documentation recommends stable HTTPS endpoints for production remote servers, plus authorisation when private data is processed or actions are performed on a user’s behalf. (OpenAI)
Conclusion
MCP servers create a standardised connection between AI applications and enterprise systems. They can supply data as context and offer functions for retrieval or updates. That gives organisations a basis for embedding AI into existing processes in a controlled way – provided they carefully define the exposed functions, permissions and security rules.