AI agents and technology
What is MCP? Definition and examples
Also: Model Context Protocol
In short
MCP (Model Context Protocol) is an open standard through which AI applications access data and tools. A system offers an MCP server once, and compatible AI agents can use it. You decide what an agent may read or change through it.
Why it matters: MCP makes your systems usable by compatible AI agents without one integration per tool.
Without MCP
One integration per tool and system
With MCP
One server, many compatible agents
Why does MCP matter for your company?
Without a standard, every combination of AI tool and system needs its own interface. With several assistants and many systems this quickly becomes a patchwork. MCP replaces it with a shared language: a server describes which data and actions it offers, and a client such as Claude or ChatGPT can use them.
For leadership MCP is above all a question of control. Per system you decide which agent may read which data and carry out which actions. A standard does not protect you by itself, though: you still have to set up rights, approvals and logs.
Example: One state, several agents
Engineering works with a coding agent, the product team with a chat assistant, sales with a third tool. Each has its own copy of the strategy. After the decision “Strategy 2027: focus on the DACH mid-market, no US expansion” IT has to update three integrations.
With MCP the approved state lives in one place and is offered through one server. All three tools read it through the same protocol. When the decision changes, you change it once, and every agent works with it from the next request.
3 common mistakes to avoid
Mistaking MCP for security
MCP governs how things connect, not who may do what. You set up rights and approvals yourself.
Connecting any server unchecked
An MCP server can read data and trigger actions. Connect only servers from trusted sources, with the fewest rights needed.
Write access from day one
Start with reading. Anything that changes data needs approval by a person.
Step by step
- 1
Choose the systems
Which data do your AI agents need most? Start there.
- 2
Set rights per agent
Read, draft or act: decide what is allowed.
- 3
Build in approvals
A person approves changes to source systems.
- 4
Log access
Record which agent read or changed what, and when.
Bring one important decision.
In 30 minutes with Marc or Simon we show where your work drifts from the decision today.
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Related terms
- AI agent →An AI agent is a program that uses a language model to pursue a goal, plan its own steps and use tools to get there.
- Enterprise Context Management →Enterprise Context Management (ECM) keeps a company's context current, reviewed and usable for people and AI agents.
- LLM data controls →LLM data controls are the technical and contractual measures that determine what happens to your data in a language model.
- Human in the Loop →Human in the loop means a person reviews and approves an AI process at defined points before it continues.