AI and MCP servers,
in plain language.
AI becomes more useful when it can work with information and tools beyond the conversation. The Model Context Protocol (MCP) gives AI applications a common way to connect to those capabilities.
What does the server do?
An MCP server exposes capabilities to an AI application: tools that perform actions, resources that supply context, and prompts that provide reusable interaction templates. For example, a server could offer a tool to query a project database, a resource describing its structure, and a prompt for preparing a report. Read the MCP server overview.
A simple example
Imagine asking an assistant to summarize a team’s recent updates. A connected server could provide a tool that retrieves those updates. The assistant uses the returned information to write a summary. The connection supplies access; the AI still has to interpret the result.
What it means for this lab
A future version of this site could expose selected operations—such as reading shared updates—through an MCP server. That would need an actual server, shared storage, and appropriate access controls. This blog post is an introduction; the demo site does not currently run an MCP server.
Start with one useful connection, keep its permissions clear, and expand as the workflow proves useful.