Short answer
An MCP server is a program that exposes tools, prompts, or resources to an AI client over the Model Context Protocol. Instead of packaging instructions for the agent to read once, it stays available as a live integration the client can call during a session.
That makes MCP servers the right abstraction for databases, browsers, file systems, SaaS APIs, issue trackers, and other capabilities that need runtime interaction.
How MCP works in practice
MCP follows a client-server design. The AI host is the client. The MCP server advertises a set of tools and optional resources. The host discovers those capabilities, lets the model select or invoke them, and sends requests over a supported transport such as standard I/O, Server-Sent Events, or HTTP.
That architecture is what allows the same server to work across multiple clients without every vendor inventing a brand new plugin protocol.
Why MCP is different from ordinary package installs
An npm package is just a package. An MCP server is a runtime integration surface. Once configured, it can receive user data, agent-generated plans, file paths, arguments, and auth material. It can also expose read-only or write-capable tools.
That is why installation guidance alone is not enough. You also need to know the permissions and trust assumptions around the server you are enabling.
Typical examples
- A browser automation server such as Playwright.
- A design integration such as Figma.
- A GitHub or Linear integration that lets the model read and update issues.
- An internal documentation or database bridge that provides search and fetch tools.
Sources and further reading
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