What MCP is, and when your product needs one

The Model Context Protocol, or MCP, is an open standard for connecting AI assistants to the tools and data they need. Anthropic published it in late 2024, and most major assistants and coding tools now support it. If your customers use an assistant at work, there’s a fair chance someone has already asked whether your product “has an MCP server”. Here’s what that means, and when it’s worth building one.

How it fits together

The assistant, such as a chat app or a coding tool, is the host. It connects to one or more MCP servers, and each server describes what it offers in a standard format. Any assistant that speaks MCP can then use any server, with no custom integration work on either side.

Servers offer three kinds of things. Tools are actions the model can call, like “create an invoice” or “search orders”. Resources are data the assistant can read, like a document or a customer record. Prompts are ready-made instructions a person can pick, like “summarize this account”. Most servers start with tools.

Local or remote

A server can run on the person’s own computer, started by the assistant and talking to it directly. That suits developer tools and anything that works with local files. Or it can run on the internet, reached over HTTP with the person signed in. That’s the usual shape for a product with customer accounts: the server acts as the signed-in person, with their permissions, and there’s nothing to install.

Why not just an API?

You may already have a public API. MCP doesn’t replace it, and a good server usually sits on top of it. The difference is the reader. An API is written for developers, who read the documentation and write code. An MCP server is written for a model, which reads the tool descriptions at the moment it needs them and decides what to call. That changes the design: fewer, clearer tools, named for the jobs people want done, with descriptions that say when to use each one.

When your product needs one

A server earns its place when people already move information between your product and an assistant by hand, copying records into a chat and pasting answers back. It also makes sense when customers ask for it during sales, or when your product holds data that makes assistants more useful, such as tickets, documents, orders or schedules.

It can wait if your product is mostly visual or real time, if customers rarely use assistants for the work your product does, or if the data is too sensitive to expose until your permissions and audit trail are ready.

Start small

The first version should cover one or two jobs well, read-only where possible. Searching and reading records is useful on its own and low risk. Actions that change data can follow once you’ve seen how people use the server and how the model behaves. Plan the security from the start, too: who the server acts as, what it can reach and what it records.

The short version

MCP is the common plug between AI assistants and the products people use. A server makes sense when customers already shuttle data between your product and an assistant. Start with a few clear, read-only tools built on your existing API, and add actions once you trust how they’re used.