The Model Context Protocol (MCP) is an open standard with which a AI assistantAn "AI assistant" is a digital application that uses artificial intelligence to support and independently complete tasks, processes, or communication. Unlike conventional digital tools, it learns... Click to learn more Secure and structured access to external tools, data sources, and services is possible. Anthropic introduced MCP on November 25, 2024, as a standard for secure two-way connections between data sources and AI-powered tools. It's important to correct a common misconception: MCP is not a general-purpose protocol for data models or simulation models. In practice, it primarily focuses on controlled tool access for AI applications such as Claude, ChatGPT, or proprietary enterprise assistants.
If you want to use AI effectively in your company, MCP (Management Communication Platform) is an infrastructure issue. An AI assistant can use MCP to, for example, read CRM data, find website content, review support cases, search internal documents, or perform preparatory steps for a proposal. However, the benefits only materialize once processes, responsibilities, data quality, and permissions are clearly defined.
That's exactly what I see time and again in SME projects: AI doesn't automatically bring order. AI amplifies existing order – or existing chaos.
Model Context Protocol: a simple definition for SMEs
The Model Context Protocol (MCP) standardizes how AI applications communicate with external systems. Without MCP, every AI application would have to create a separate protocol for every tool, database, and other system. APIAn API explained simply: An API is a standardized programming interface through which two software systems exchange data or functions. The full name, Application Programming Interface, describes... Click to learn more They can be connected individually. MCP provides a common framework through which an MCP client can communicate with an MCP server and use tools, resources, and prompts.
MCP is a standardized connection between AI assistants and enterprise systems: not the intelligence itself, but the controlled connection to knowledge and courses of action.
This distinction is important for small businesses. MCP doesn't replace strategy, process analysis, or meticulous data management. However, MCP can help make digital processes less fragmented—for example, in the interplay between website, knowledge base, sales process, and customer communication. If you want to fundamentally build these systems, then MCP falls more into the category of... AI & Digitalization than in an isolated tool integration.
The most important building blocks of MCP
The official MCP specification describes hosts, clients, and servers as the central communication roles. Servers can also offer clients the features resources, prompts, and tools. In practical terms, you can think of the building blocks like this:
- MCP Host: The AI application you are working in, for example a chat interface, a development environment, or an internal AI assistant.
- MCP Client: The connection component within the host that communicates with one or more MCP servers.
- MCP server: The service that provides data, functions, or workflows, such as a CRM system, a file storage system, or an internal knowledge base.
- Tools: Executable functions that the AI assistant can use include, for example, "retrieve customer data", "check ticket", or "prepare calendar entry".
- Resources: Context and data that can be read or used, such as documents, database entries, or product information.
- Prompts: Templates for recurring tasks, workflows, or structured requests.
- Transport: The technical type of connection, such as local or via HTTP-based communication.
- Authentication and permissions: The rules governing who is allowed to access which data and functions.
- Logging: The logging of which requests, tool calls and data accesses have taken place.
OpenAI supports MCP in its Responses API via the built-in tool type "mcp". This allows OpenAI models to access additional tools and external services via connectors or publicly accessible remote MCP servers. This demonstrates that MCP is not solely an Anthropic topic, but a more broadly usable connection standard for AI tool access.
Benefits: What MCP specifically makes possible in small businesses
In an SME, MCP can help reduce recurring information and process breaks. For example, an AI assistant could read relevant customer data from a CRM system, retrieve suitable text modules from a knowledge base, and use this information to prepare a draft proposal.
Another use case is the website: An assistant can use defined resources to find and structure content and make it usable for internal work – especially when the The website is technically and content-wise well-structured. at a hunt.
The economic value does not lie in the technology itself. The value lies in reduced search effort, fewer media breaks, faster preparation, and more transparent processes. Therefore, at Berger+Team, we do not view MCP in isolation, but as part of a larger system. strategic consulting, process clarification and responsible AutomationAutomation is the execution of recurring tasks and rule-based processes by software, systems, or machines, ensuring that a process continues reliably without constant manual intervention. The... Click to learn more.
Security: Access to a tool means power to act
MCP can read data and execute tools. Therefore, MCP needs clear security rules. A poorly planned MCP server can expose sensitive information or enable actions that should never have been automated. For SMEs, the most important rule is: Never grant an AI assistant more rights than the specific work process actually requires.
- Least Privilege: Each MCP client and each MCP server receives only the minimum necessary rights.
- Data minimization: Only the data required for the task will be transmitted.
- User permissions: Sensitive tool calls require conscious confirmation by a human.
- Logging: Tool calls, data accesses, and errors must be logged in a traceable manner.
- Auditability: A audit trail It should show who or which system triggered which action and when.
- Separation of sensitive systems: Accounting data, personnel files, or critical customer data should not be carelessly sent to a general AI assistant.
From my perspective, this isn't a hindrance, but a responsibility. Good automation empowers people, relieves the burden on teams, and clarifies processes. Poor automation shifts risks into systems that are difficult to control. If you want to streamline processes step by step, Automation Always an organizational decision first – and only then a technical one.
Demarcation: MCP, API, RAG and Agent Descriptor Files
An API MCP is a technical interface between software systems. It can utilize APIs, but above all, it standardizes how an AI assistant can discover and use many different interfaces, tools, and data sources.
RAG RAG stands for Retrieval-Augmented Generation. RAG extracts knowledge from documents or data repositories and incorporates it into a response. MCP goes further: MCP can not only provide context but also trigger actions via tools – depending on permissions.
Agent Descriptor Files MCP typically describes the capabilities, rules, or context of an agent. MCP operationally connects the agent to systems, data, and tools. In short: Agent Descriptor FilesAgent descriptor files are structured description files that a software-based agent uses to disclose its capabilities, rules, interfaces, inputs, outputs, and operational boundaries in a machine-readable format. Put simply: This is how... Click to learn more MCP describes what an agent should know or be able to do; it governs how the agent is controlled and connected to tools and resources.
FAQ
What is MCP?
MCP stands for Model Context Protocol. This protocol standardizes how an AI assistant can access external tools, data sources, and services without having to build a custom solution for each integration.
What is an MCP server?
An MCP server provides an MCP client with specific functions, data, or templates. For example, an MCP server can offer access to documents, CRM data, internal search capabilities, or clearly defined tools.
What is the difference between MCP, API and RAG?
An API is a single technical interface, RAG delivers knowledge from documents, and MCP standardizes the tool access of an AI assistant. MCP can therefore integrate APIs and RAG-like knowledge sources, but it has a broader scope.
Is MCP safe?
MCP is only as secure as its concrete implementation. You need clean permissions, least privilege, data minimization, approvals, logging, and auditability to prevent tool access from becoming uncontrolled power.
When does MCP become worthwhile for SMEs?
MCP is worthwhile if your company needs to access multiple data sources, tools, or internal documents on a recurring basis. MCP becomes particularly useful when an AI assistant is not only required to answer questions, but also to prepare structured work or support secure process steps.
What mistakes should you avoid when using MCP?
The biggest mistake is seeing MCP as an abbreviation for unclear processes. First clarify data quality, responsibilities, approvals, and security boundaries – then MCP can help create less chaos and better processes.
