An MCP client is the connector built into an AI application that talks to one MCP server on the model's behalf. It opens the connection, discovers the tools and data the server offers, forwards the model's tool calls, and passes the results back into the conversation.
Every setup built on the Model Context Protocol has three parts: a host application, a client inside it, and a server outside it. The host is the app you actually use — an AI chat app, a coding assistant, an autonomous agent. Inside that host runs the client, and it keeps a dedicated one-to-one link to each server it connects to. If your assistant is wired to three servers, it is running three clients.
The client never decides what to do; the language model does. Its job is plumbing: negotiate the protocol version, list what the server can do, execute the calls the model requests, and hand back structured results the model can read.
Say you use a desktop AI assistant and ask, "Summarise the three newest PDFs in my Reports folder." The host passes your request to the model, which asks the client what tools are available. The client, connected to a filesystem server, reports back options such as "list_directory" and "read_file". The model calls "list_directory" on the Reports folder; the client runs it against the server and returns the file names; then the model calls "read_file" three times. Only after the client streams those results back does the model write your summary. You see one clean answer — underneath, the client made four round trips.
As people increasingly research brands and products inside AI assistants, MCP clients are part of how those assistants reach live, external information instead of relying only on training data.