Model Context Protocol

MCP (Model Context Protocol) is an open standard that helps AI applications connect to external tools and information. It gives an application a consistent way to discover what a connected service can provide and request it when needed.

Imagine asking an assistant to check a project’s status. To answer, it may need access to a task management system. If you then ask it to schedule a meeting, it needs a different connection. MCP provides a common way for the assistant’s application to communicate with services that offer those capabilities.

An MCP connection has three main parts: a host, a client, and a server. The host is the application you interact with. It creates an MCP client to communicate with a particular server. The server makes information or capabilities available to the application.

An MCP server can provide tools, resources, and prompts. A tool lets the application request an action, such as searching records or creating a task. A resource provides information the application can read. A prompt provides a reusable template for a particular interaction. A server does not have to offer all three.

For example, a company could connect an AI application to a project management system through an MCP server. The application might read the latest project details, identify overdue tasks, and prepare a status summary. If the server also offers a tool for updating tasks, the application could request that action when appropriate. What it can actually see or change depends on the connection and its permissions.

This is particularly useful for an AI agent that needs to work across several systems. Without a shared protocol, developers may have to build and maintain a separate integration for every combination of application and service. MCP provides a common interface, although each connection still needs to be configured and tested.

MCP is sometimes mentioned alongside Retrieval-Augmented Generation, but they solve different problems. Retrieval-Augmented Generation is a method for finding relevant information and giving it to a model before it answers. MCP is a protocol for connecting an AI application to external capabilities. An application might use an MCP server to access a search tool that forms part of a retrieval workflow, but MCP itself is not a retrieval method.

Using MCP does not automatically make an AI system reliable or secure. A connection may expose sensitive information or allow actions that change business records. Teams need to decide which servers to trust, what each user is allowed to access, which actions require approval, and how tool use will be logged and reviewed.

MCP makes connections between AI applications and external systems more consistent. The practical value comes from choosing useful capabilities, setting appropriate permissions, and checking that the application uses them correctly.

FAQs

Is MCP an AI model?

No. MCP is a protocol for communication between an AI application and external services. The model may help decide when information or a tool is needed, but MCP does not generate answers or make decisions on its own.

What is an MCP server?

An MCP server is a program that makes specific tools, resources, or prompts available to an AI application through the protocol. For example, a server might let an application search a document collection or request an update to a task management system.

Does MCP replace APIs?

No. An MCP server can use an existing API behind the scenes. MCP gives AI applications a consistent way to discover and request capabilities; the connected service may still rely on its own API to perform the work.

Is it safe to connect an AI application to an MCP server?

That depends on the server, its permissions, and how the application handles requests. Before connecting a server, teams should check what information it exposes, what actions it allows, how users are authorised, and whether sensitive actions need human approval.