What is an MCP server and why a business needs one

An AI assistant becomes truly useful when it can see your data and act in your systems: check an order in the CRM, look up stock, create a ticket. Until recently, each such connection had to be built separately for each AI application.

MCP, the Model Context Protocol, is an open standard that addresses this. Its documentation compares it to USB-C: one common connector instead of a separate cable for every device.

What an MCP server is

MCP has three participants:

• Host. The AI application the person works with, such as an assistant or an agent.
• Client. The part of the host that maintains the connection to a server.
• Server. A small program that exposes one system, for example the CRM or a document store, in a standard form.

A server offers three kinds of things:

• Tools: actions the model can call, such as "find customer" or "create ticket".
• Resources: data that can be read, such as files or records.
• Prompts: ready-made templates for typical tasks.

A server can run locally on the user's computer or remotely over HTTP. For company use, the remote option is more common, because access can then be managed centrally.

What it gives a business

Without a standard, every combination of AI application and company system requires its own integration. With five applications and ten systems, that means up to fifty connections to build and maintain.

With MCP, each system needs one server, and each AI application that supports the protocol can use it. In practice this gives you:

• Less integration work. A new AI application reuses the servers you already have.
• Freedom to change. You are less tied to one AI vendor, because the connections do not belong to it.
• One point of control. Rights, logs and limits for a system live in one place, rather than being scattered through many applications.

MCP compared with an API and RAG

MCP does not replace your API. A server usually calls an existing API or database and describes what is available in a way the model can understand. An API is written for developers who read the documentation in advance. MCP lets a model discover the available tools and how to use them while it works.

MCP does not replace RAG either. RAG finds knowledge in documents, and MCP connects a model to systems and actions. They work well together: document search can itself be offered as a tool on an MCP server.

Examples of use

• Sales. The assistant finds a customer's history in the CRM before a call and prepares a summary.
• Analytics. The model runs read-only queries against a data warehouse and explains the result.
• Support. An agent checks the order status and drafts a response for an employee to approve.
• IT. The assistant finds an incident and related changes in the ticket system.

Security comes first

An MCP server gives a model access to real systems, so it must be treated like any other access to production data.

• Authentication. Remote servers should verify who is connecting. The MCP specification describes authorization based on OAuth for this.
• Minimum rights. Actions should run with the rights of a specific user or a limited service account, not an administrator.
• Read-only first. Begin with reading, and add write tools one at a time.
• Approval for risky actions. Deletions, payments and messages to customers should require confirmation from a person.
• Logs. Record who called which tool, with what parameters and what result.
• Untrusted content. Data returned by a tool may contain hidden instructions for the model. This is prompt injection, and the protection is to limit what the model can do.
• Third-party servers. A ready-made server is code running with your data. Check its source and maintainer, and fix the version you use.
• Narrow tools. A few precise tools are safer and work better than one tool that accepts any SQL.

When you do not need MCP

If you have one AI application and one integration that rarely changes, a direct API call is simpler. MCP pays off when there are several systems or applications, or when you expect them to appear.

Where to start

1. Choose one system and one task where the model clearly lacks data.
2. Check whether a reliable server for that system already exists.
3. Define a small set of read-only tools with clear descriptions: the model chooses tools based on these descriptions.
4. Set up authentication and logs from the start.
5. Test on real requests, then add write actions with approval.

Summary

MCP is a common way to connect AI to company systems: one server per system instead of a separate integration for each application. The protocol itself is simple. The real work lies in choosing the right tools, setting rights and keeping everything under control.

To connect your systems, see our MCP and AI integration service.

Sources

• Model Context Protocol, official documentation
• MCP authorization, specification