What Is MCP (Model Context Protocol)? The Standard Way for Businesses to Connect AI to Company Data

In brief: MCP (Model Context Protocol) is an open source standard for connecting AI applications to external systems. According to the official site, with MCP AI applications such as Claude or ChatGPT can connect to data sources (local files, databases), tools (search engines, calculators) and workflows (specialised prompts). The official analogy: “Think of MCP like a USB-C port for AI applications”: just as USB-C offers a standard way to connect electronic devices, MCP offers a standard way to connect AI applications. Assistants like Claude and ChatGPT and development tools like Visual Studio Code and Cursor support it; the goal is “build once, integrate everywhere.” Anthropic says it has donated MCP to the Agentic AI Foundation under the Linux Foundation (details). This article explains MCP in business language. Source: modelcontextprotocol.io (26 October 2026).

Last updated: 26 October 2026

Why does MCP exist?

An AI model on its own answers only from what it “knows.” To be useful it has to connect to your data and tools: calendar, CRM, ERP, file sharing, code repository. Before MCP you had to write a separate integration for every AI application and every system. MCP aims to bring that down to one common protocol.

A simple analogy: Imagine an office where every device needs a different cable. With USB-C there's one type of connection. MCP is that common “cable” connecting AI applications and systems.

How does it work? (a plain account)

The structure in the official documentation rests on the roles of two sides:

• MCP client: The AI application (Claude, ChatGPT, VS Code, Cursor etc.).
• MCP server: A program that exposes data or a tool to the AI. For example, a server offering “product search in the company CRM” or “reading the calendar.”

The client learns from the server which tools, data and workflows it offers and uses them according to the user's request. In other words, the AI receives “here is what I can do in this system” in a standard form.

Examples on the official site

• Agents can access your Google Calendar and Notion, acting as a more personalised AI assistant.
• Claude Code can generate an entire web app using a Figma design.
• Enterprise chatbots can connect to multiple databases across an organisation, letting users analyse data through chat.
• AI models can create 3D designs in Blender and print them on a 3D printer.

Benefits for businesses

Three concrete gains from a business point of view:

1. Fewer custom integrations: The same MCP server can be used with several AI applications.
2. Less vendor dependence: Even if you change the AI application, your system connections can largely stay the same. See our model selection framework.
3. A fast pilot: You can start from ready-made MCP servers to try AI with company data.

Example use cases

• Sales: “Summarise this customer's last three orders and open quotes” (an MCP connection to the CRM).
• Support: Searching a knowledge base and drafting a reply.
• Finance/ERP: Read-only report queries.
• Software team: An AI assistant with the code repository and issue tracker.
• E-commerce: Product search, stock checks and cart: our agentic commerce article.

Security: the part that needs the most attention

MCP gives an AI application access to company data and tools; that is an important security decision. The official site also points to a “Security” best practices section. Practical risks:

1. Excess authority: If a server grants more permission than needed, the AI can read unneeded data or take actions.
2. Untrusted servers: Connecting an MCP server from an unknown source is like installing unknown software. Incidents like the LiteLLM attack showed the toolchain is an attack surface.
3. Prompt injection: Instructions hidden in content the AI reads (email, documents, web) can steer the AI into unwanted actions with MCP tools.
4. Data leakage: Sensitive data may be sent to the model provider; the provider's data policy applies.
5. Identity ambiguity: Under whose authority is the AI acting?

For measures, use our 8 controls for giving agents access to company data and our AI use policy guide.

Getting started with MCP: 7 steps

1. Choose a use case: A low-risk, read-only job (e.g. knowledge base search).
2. Classify your data: Which data may be opened to AI?
3. Use servers of known origin: From official or trusted sources; if you write your own, design it with least privilege.
4. Give a separate identity and least privilege: The server should be able to do only what's needed.
5. Keep records: Which tool was called, and when?
6. Add write authority later: Read first, then approved writes.
7. Measure and review: Time saved, errors, security incidents.

Common mistakes

• Assuming “it's a standard, so it's safe:” A standard does not guarantee security.
• Connecting directly to production data with full authority.
• Not knowing who wrote the servers.
• Granting write/delete permission without human approval.

MCP and AI integration with Aktaş Digital

We set up secure AI connections to your company systems (CRM, ERP, e-commerce, documents) and design the authority model and record-keeping. To get a scope and cost breakdown, use our quote form. Related services: AI solutions.

Frequently Asked Questions

Who owns MCP?

According to the official site it is an open source standard. Anthropic says it donated MCP to the Agentic AI Foundation under the Linux Foundation.

Which applications support MCP?

The official site lists AI assistants like Claude and ChatGPT, development tools like Visual Studio Code and Cursor, and others among supporters.

What is the difference between MCP and an API?

APIs are usually specific to one service. MCP offers a layer for AI applications to connect to different systems in a common form.

Do I need to know how to code?

Using ready-made MCP servers may need technical knowledge; writing your own needs software skills. I recommend getting technical support on authority and security matters.

Is MCP secure?

Security depends on the authority you give, the origin of the server and your monitoring. Being a standard does not by itself provide security.

Conclusion

MCP is a candidate to be the standard way of connecting AI applications to company data and tools. The opportunity: fewer custom integrations and vendor flexibility. The risk: excess authority and untrusted servers. First step: choose a single low-risk, read-only scenario you want to connect AI to and list in writing which data it would access.