What Is MCP? What You Need to Know Before Connecting AI to Your Company Data

Wherever AI agents are discussed, the abbreviation MCP now comes up. This post focuses less on the technical detail of the concept and more on the question a business owner should ask: if I connect AI to my data, what am I putting at risk?

What is MCP?

The Model Context Protocol (MCP) is an open protocol that standardizes how AI applications connect to external tools and data sources. Anthropic announced the protocol on 26 November 2024. At the end of 2025 it handed it over to the Agentic AI Foundation under the Linux Foundation. Today many AI applications, including Claude and ChatGPT, support MCP.

The logic is simple: instead of writing a separate integration for each tool, an AI application connects to resources such as a calendar, a CRM or a file store in the same way through MCP. Much like USB-C being a single port for different devices.

What does it mean for a business?

What sets an AI agent apart from a chatbot is that it can take actions on your behalf. MCP makes it easier to set up this ability to act: through the tools it is connected to, the agent can open appointments, read records and prepare email drafts.

If you use n8n, this helps you directly. The MCP Server Trigger node in n8n exposes the tools in a workflow to MCP clients. The MCP Client Tool node lets an AI agent use tools on external MCP servers.

Easier connections, growing risk

Once you give a model tools, a mistake can stop being a wrong answer and become a real action. The risks that stand out in security research are these:

• Prompt injection: Instructions hidden inside an email, document or web page that the model reads. In an agent that uses tools, this can mean more than a wrong answer: an unauthorized action being carried out.
• Tool poisoning: Malicious instructions embedded in tool descriptions. The model sees these descriptions; the user normally doesn't.
• Excessive permissions: If a connection is set up with broader read and write permissions than necessary, a mistake by the model or a compromise can spread to all of your data.
• Untrusted MCP servers: A third-party server can run with your permissions. If access keys sit in plain-text config files, there is a risk of leakage.

The KVKK dimension

Customer data sent to an AI model via MCP may be processed on the model provider's servers. This can mean the data is transferred abroad. That's why you need to know which data goes to the model and, if necessary, have a legal assessment made. For details, see our KVKK-compliant automation article. The simplest safeguard is never to send fields such as national ID numbers, IBANs and tax numbers to the model.

Six questions before connecting

1. Which data will the agent be able to read? Give only what its job requires (least privilege).
2. Is write permission really needed? Starting with read-only in the first phase is safer.
3. Is there human approval for hard-to-reverse actions? Put actions such as deleting, payments and sending email externally behind approval.
4. Where does the MCP server come from? Don't connect a server whose source you don't know to company systems.
5. Will you know if a tool description changes? A tool description that changes silently is one of the routes for tool poisoning.
6. Is a log kept of who accessed what and when? Without traceability you can't find out what went wrong when a problem occurs.

FAQ

Sources: Anthropic, Introducing the Model Context Protocol · n8n Docs, MCP Server Trigger · n8n Docs, MCP Client Tool · Simon Willison, Model Context Protocol has prompt injection security problems