Model Context Protocol (MCP) defines a common way for an AI application to discover and use capabilities exposed by a server. Instead of writing a separate custom integration for every compatible client, a developer can expose tools or resources through an MCP server and connect them to supported clients.
A typical use is connecting an AI assistant to a team's tools: a documentation server for search, an issue tracker server for creating tickets, a database server for read-only queries. Because the protocol is shared, the same server can work with more than one compatible client. Treat MCP as the connector standard and keep your own checks on what each connected tool is allowed to do.
MCP standardizes communication; it does not automatically make a server trustworthy or secure. Review permissions, validate inputs, protect credentials and expose only the actions a client needs. Related: MCP Server, MCP Client, Tool Calling.