What Is MCP and How to Use It With AI

What is MCP, and how do you use it with AI?

MCP, the Model Context Protocol, is an open standard from Anthropic that defines a common way for AI applications to connect to external tools, data sources, and actions. Instead of writing a bespoke integration for every model and every data source, you expose your capabilities once as an MCP server, and any MCP-aware AI client — such as Claude Desktop, an IDE assistant, or your own application — can use them. You use it by running or installing an MCP server and pointing a compatible client at it.

The analogy people reach for is a universal port. Before a common standard, every device needed its own cable; afterwards one connector worked everywhere. MCP aims to be that connector between AI assistants and the tools they need.

The pieces: servers, clients, and what they exchange

• MCP server — a small program that exposes capabilities: tools the model can call, resources it can read, and prompts it can use. You might run a server for your database, your file system, or a third-party API.
• MCP client — the AI application that connects to servers and makes their capabilities available to the model.
• Tools, resources, and prompts — the three things a server can offer: actions to perform, data to read, and reusable prompt templates.

The model still decides when to use a tool, exactly as in ordinary function calling. MCP's contribution is standardising how that tool is described and connected, so the integration is reusable instead of one-off. Think of the difference between writing a custom driver for every printer and agreeing on a single print standard every device and application can speak. The model is still doing the work; MCP just removes the need to rebuild the connection for each new combination of assistant and data source.

How to start using MCP

1. Pick an MCP-aware client. Claude Desktop supports MCP, and a growing number of editors and agent frameworks do too.
2. Add an existing server. There are ready-made servers for common needs — file systems, Git, databases, web search — that you configure and the client picks up.
3. Point the client at the server through its configuration, and the tools appear for the model to use.
4. Ask the assistant something that requires the server, and watch it call the tool and use the result.

The quickest way to understand MCP is to add one prebuilt server to a client you already use and ask a question that forces it to reach for the new capability.

Building your own server

When no existing server fits, you write one. Official SDKs in Python and TypeScript handle the protocol details, so your job is to define the tools — their names, descriptions, and parameter schemas — and implement what each one does. A tool might query your internal API, read a record, or trigger a workflow. Because the server speaks the standard protocol, that same server then works with every MCP client, not just the one you tested against. That reusability is the entire point: build the integration once, use it everywhere.

Keep the server's boundary tight

Because an MCP server can expose real actions and real data, it is a security boundary, not just a convenience. The model on the other side may be steered by user input you do not fully control, so the server must defend itself rather than assume good behaviour.

• Expose only the tools a given client actually needs, not your entire internal surface area by default.
• Validate every tool argument and enforce your own permission checks before doing anything.
• Be especially careful with tools that write, delete, or spend, and require confirmation where the stakes are high.
• Keep any credentials the server uses on the server, scoped to the minimum access required.

Treat the server as you would any public-facing API, because in effect that is what it is.

Why MCP matters for real products

For a business, MCP reframes AI integration from a pile of model-specific glue into a clean, reusable layer. Expose your systems as MCP servers and you can connect them to whichever assistant or agent you choose today, and a different one tomorrow, without rebuilding. It also helps keep the boundary clear: your server controls exactly what the model can see and do, which is where access control and validation belong. As the standard matures, building your integrations MCP-first is a sensible hedge against being locked to any single vendor's proprietary plugin format.

Prefer it built for you?

MCP is young, powerful, and worth getting right — especially the server side, where your tools, permissions, and data boundaries live. Talk to BSH Technologies about our software engineering services and we will build MCP servers that connect your systems to AI cleanly and safely.