Measure how much tool schemas add to every AI request

Why schemas become expensive

Every tool name, description, parameter, enum and example contributes to input. Long descriptions copied across many tools can dominate small user prompts. Nested JSON and verbose examples add further overhead.

Safe optimization order

First remove tools that cannot be called for the current intent. Then deduplicate shared wording and shorten descriptions while preserving parameter semantics. Keep safety-critical constraints and exact enum values.

Validate agent behavior

A smaller schema is useful only when the agent still selects the correct tool and constructs valid arguments. Varion’s tool pruning strategy can be tested against required tool-call outcomes before production use.

Measurement checklist

1. Choose a representative completed task, not an artificial one-line prompt.
2. Record the selected model, provider input, cached input, output, retries and final result.
3. Change one optimization mechanism at a time so the cause remains visible.
4. Verify required identifiers, tool calls, code changes or business fields.
5. Keep passthrough available when the reduced request does not pass.

How Varion fits

Varion Token Engine is a gateway and testing platform for reducing eligible input-token waste across supported AI traffic. It reports original and provider-bound input, keeps provider charges separate, and does not claim that every request can be reduced. New verified users receive 100,000 processed input tokens and 50 local test runs.