An AI agent does not run its tools by itself. It reads a list of tool definitions, picks one, and fills in the arguments. Your code then makes the call. When the list holds five tools, that choice is easy. When it holds sixty, from four connected servers, the model picks the wrong tool more often. The wrong tool usually returns a normal, successful response.

Research from the last three years explains why. Long prompts are read unevenly, similar tools are hard to tell apart, and selection accuracy falls as the list grows. This article summarises that evidence and the habits teams use to keep the tool list short.

A tool definition has three parts: a name, a description, and a schema for its arguments. The model never sees your tool's code. It sees only that text, inside its context window, beside the system prompt and the conversation.…