Function Tool Definition Token & Cost Overhead Calculator
Measure how many tokens your tool/function definitions consume in the system prompt, then convert that into per-request and at-scale cost across major LLM providers. Paste your schema, pick a model, and see the overhead live.
Overhead breakdown
| Component | Characters | Tokens |
|---|---|---|
| Raw schema text | 0 | 0 |
| Provider markup overhead | 0 | 0 |
| Total tool overhead | 0 | 0 |
Estimates are heuristic. Real token counts vary by tokenizer and provider encoding — validate with the official tokenizer for exact billing.
Why tool definitions cost you money
Every function or tool you expose to an LLM has to be declared in the request. Providers serialize your JSON schema into the prompt and re-send the entire tool set on every single call. If you register 12 tools totaling 3,000 tokens and you fire a million requests a month, those definitions are billed as a million input tokens × 3,000 — a recurring, invisible tax that grows linearly with traffic and tool count.
This tool calling token overhead calculator quantifies that tax. It estimates the input-token footprint of your schema from character length, applies a provider markup factor to account for the control tokens each API wraps around definitions, then converts tokens to dollars using the selected model's input price. Switch the model dropdown and the per-request and monthly figures update instantly, so you can compare, say, GPT-4o against Gemini 2.0 Flash before committing to an architecture.
The biggest lever is rarely the model — it is tool sprawl. Trimming verbose descriptions, dropping unused tools per agent, and batching only the tools a given request needs can cut overhead by 50-90%. For multi-turn agents, also consider prompt caching (Anthropic, Google, and OpenAI all support it), which discounts repeated tool-prefix tokens heavily. Use this calculator to baseline before and after any optimization so the savings are concrete.