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Equation 17 · The Token Tax of Giving a Model More Tools

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T(n)≈τ0+nsˉT(n) \approx \tau_0 + n\bar{s}

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This equation gives an approximation: it relates the quantities while allowing an approximation. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.

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TT

Symbol T

T is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

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nn

Symbol n

n is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

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τ0\tau_0

Symbol tau_0

tau0u_0 is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

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sˉ\bar{s}

Symbol bars

bars is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

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≈

≈

Approximately equal to; the equality is not exact.

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addition

addition

Add the term after the plus sign to the term or group before it.

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subscript

subscript

The lower label selects a particular version, component, or indexed member of the quantity. For example, x₀ and xₜ can be values at different positions.

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What the article says around this equation

Anthropic’s Tool Search Tool is a direct, documented implementation of that idea. The mechanism is described plainly: “You provide all your tool definitions to the API, but mark tools with deferlr_loading: true to make them discoverable on-demand,” with a lightweight search tool — built-in options include BM25-based and regex-based search — querying an index of names and descriptions, and the full definition of a matching tool injected into context only once the model actually requests it [ 5 ] . The search tool itself is not free, but its overhead is small and fixed rather than proportional to catalogue size: “Only the Tool Search Tool loaded upfront (~500 tokens)” [ 5 ] . Applied to the same…
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Anthropic’s Tool Search Tool is a direct, documented implementation of that idea. The mechanism is described plainly: “You provide all your tool definitions to the API, but mark tools with deferlr_loading: true to make them discoverable on-demand,” with a lightweight search tool — built-in options include BM25-based and regex-based search — querying an index of names and descriptions, and the full definition of a matching tool injected into context only once the model actually requests it [ 5 ] . The search tool itself is not free, but its overhead is small and fixed rather than proportional to catalogue size: “Only the Tool Search Tool loaded upfront (~500 tokens)” [ 5 ] . Applied to the same 58-tool, roughly 55,000-token example described above, that reframes the earlier formula: instead of the full T(n) ≈\approx τ0\tau_0 + nsˉ\bar{s} , a session that only ever actually needs m of the n available tools pays approximately τ0\tau_0 + σ\sigma + msˉ\bar{s} , where σ\sigma is the search tool’s own fixed overhead. The fraction of the original tax avoided is

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