What does this equation mean?

That leaves the second meaning, and it is where the real cost lives. To discover what a server can do, a client still sends a tools/list request, and that response is the full tool catalogue — the same names, descriptions, and JSON Schemas that later get folded into T(n) on every inference call. The specification requires this operation to support pagination: “Pagination in MCP uses an opaque cursor-based approach, instead of numbered pages,” page size is “determined by the server,” and clients “ MUST NOT assume a fixed page size,” with the documentation naming the reason directly — pagination “is especially important when connecting to external services over the internet, but also useful for local integrations to avoid performance issues with large data sets” [ 2 ] . A very large catalogue therefore costs latency twice over on first discovery: once as however many sequential tools/list round trips it takes to walk the paginated cursor to its end, and again as the token cost of every schema once assembled into a request. The specification also asks servers to “return tools in a deterministic order,” and states why in language aimed squarely at cost rather than correctness: deterministic ordering “enables clients to reliably cache the tool list and improves LLM prompt cache hit rates when tools are included in model context” [ 1 ] . That is a wire-format requirement written for the economics of the thing consuming it, not for the protocol’s own bookkeeping.