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Equation 30 · The Token Tax of Giving a Model More Tools

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σ\sigma

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σ\sigma

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Putting the pieces together gives a short, honest account of where a tool protocol’s overhead actually lives. A single request pays a fixed tool-use tax τ0\tau_0 the moment any tool is attached, plus roughly sˉ\bar{s} tokens for every one of the n tools visible in that request, repeated on every turn because the underlying inference API carries no memory between calls. Lazy discovery — Anthropic’s Tool Search Tool, or a disciplined use of MCP’s own paginated listing — replaces the n in that formula with a much smaller m , the tools actually retrieved, at the cost of a small fixed search overhead σ\sigma . Separately, and not reducible to a token count at all, the choice the model makes over…
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Putting the pieces together gives a short, honest account of where a tool protocol’s overhead actually lives. A single request pays a fixed tool-use tax τ0\tau_0 the moment any tool is attached, plus roughly sˉ\bar{s} tokens for every one of the n tools visible in that request, repeated on every turn because the underlying inference API carries no memory between calls. Lazy discovery — Anthropic’s Tool Search Tool, or a disciplined use of MCP’s own paginated listing — replaces the n in that formula with a much smaller m , the tools actually retrieved, at the cost of a small fixed search overhead σ\sigma . Separately, and not reducible to a token count at all, the choice the model makes over whatever shortlist it is shown carries its own error, decomposable into a retrieval gap that shrinks as the shortlist widens and a confusion gap that grows as it does, with namespacing removing one specific, avoidable source of confusion — a same-named collision — before the shortlisting step ever runs. Schema minimization shrinks sˉ\bar{s} directly; it does not touch n or the selection error at all. Four separate levers, four separate places in the same formula, and none of them a substitute for any other.

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