These days an AI assistant does more than answer questions. It reads and summarizes your email, finds an open slot on your calendar to book a meeting, and pulls the documents it needs out of your company’s internal systems. For any of this to work, something has to connect the AI to all those outside services — a shared way of plugging in.
Late in 2024, a standard for that connection arrived: the Model Context Protocol, or MCP. Before such a standard existed, wiring an AI up to each new service meant building a custom adapter every single time. MCP promised to collapse all of that into one connector — the USB-C of the AI world, as people began calling it. Email and calendars, then collaboration tools like GitHub, Notion, and Slack, lined up to join, and within months nearly every software company had stamped “MCP supported” onto its feature list.
And yet the dissenting voices came from the developers actually using it. The trouble is the AI’s working memory. A model can only hold so much at once — think of it as the size of a desk. Connect a tool, and that tool’s instruction manual claims part of the desk before any work has begun. Today’s models can juggle a book or two’s worth of text in a single sitting, but the manuals for a few dozen connected tools alone could swallow something like ten percent of that space. The room left to do the actual work shrinks. It is like sitting down at a restaurant to find ten menus spread across the table and nowhere to set the food. Speed was a problem too: in one measurement the same task ran two to three times slower than the conventional route, and the gap was far wider on the very first connection. Mid-task disconnections were common.
That problem, though, is being patched quickly. Instead of laying out every manual up front, an AI now fetches one only when a task actually calls for it. That single change cut the space eaten by tool descriptions by as much as 85 percent. And the payoff was not only reclaimed room: when too many tools pile onto the desk, an AI tends to fumble and reach for the wrong one — yet letting it pull out only what it needs actually made its choices more accurate in tool-heavy setups. It is the difference between stacking every book in the library on your desk and simply asking the librarian for the one you mean to read. As a result, you can keep a dozen tools connected and barely feel the lag at the start of a conversation.
None of this makes MCP a cure-all. Plenty of jobs are handled perfectly well by the old habit of typing commands directly, and the argument over which approach is more efficient rolls on. Still, for ordinary users who never touch the command line — the text window known as a terminal — MCP is far more comfortable, and for services that ship no command-line tool at all, MCP is the only way in. The two approaches are drifting toward the same idea: load it only when you need it. To the person using it, the connection standard stays invisible. But if you hook an outside service into your AI assistant and notice it slowing down or dropping the link, behind the scenes sits exactly this kind of tug-of-war — the push to do more with less room.