Token counter for LLM text

Supply text to request a language-model token estimate, optionally naming the model. Review the returned count and its context when preparing a prompt or comparing text drafts.

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Use Toolmu count-model-tokens-strale to estimate the supplied text. Ask the user to paste the text if missing, and preserve all wording, spaces and line breaks. If the user gives an AI name, pass that exact name; do not guess another name or model. If none is given, leave it unspecified rather than choosing a default. Inspect the actual response and report its count and any returned explanation. State when the response does not say which AI or counting method it used. Distinguish an estimate from a bill or Toolmu charge, and do not claim exact counting, model support, current pricing, or that the full request fits. Reuse the user’s Toolmu connection or follow https://toolmu.com/SKILL.md to connect. Check current availability, price and budget before calling, and stay within the authorized amount.

Task:
Estimate how much room [the text I share] may take up when used with AI. If I provide [the AI I use], include its name; otherwise, do not guess which AI the estimate applies to. Explain the returned number in everyday words, and make clear it is an estimate, not a bill.

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Connection guide

Up to 0.028512 credits / call

The final charge is based on confirmed usage. Unused reserved credits return to your available balance.

Strale

Token counter for LLM text

Supply text to request a language-model token estimate, optionally naming the model. Review the returned count and its context when preparing a prompt or comparing text drafts.

You receive

Review any token count returned for the text you supplied. Your Agent can keep the count beside that text and your requested model, retaining any model or estimation notes in the response so you can interpret the figure.

Start with your task

Estimate how much room [the text I share] may take up when used with AI. If I provide [the AI I use], include its name; otherwise, do not guess which AI the estimate applies to. Explain the returned number in everyday words, and make clear it is an estimate, not a bill.

What to know

An estimate does not establish provider-billed usage or guarantee that a complete model request fits its context limit. Model handling and the counting method must be checked for your intended use.

Inputs & outputs

Inspect the current inputs, availability, and maximum charge before starting.

Input schema

Input schema
Field / Path	Type	Requirement	Meaning
queryParams`queryParams`	`object`	Required	Required fields `text`
Text to count`queryParams.text`	`string`	Required	The text for the token estimate. Preserve its content, spacing and line breaks.
Model identifier`queryParams.model`	`string`	Optional	Optional model identifier. The input description gives gpt-4o and claude-3-haiku as examples; check the response for how your requested model was handled.

View raw JSON

{
  "type": "object",
  "required": [
    "queryParams"
  ],
  "properties": {
    "queryParams": {
      "type": "object",
      "required": [
        "text"
      ],
      "properties": {
        "text": {
          "type": "string"
        },
        "model": {
          "type": "string",
          "description": "Model identifier, such as gpt-4o or claude-3-haiku."
        }
      }
    }
  },
  "additionalProperties": false
}

Result schema

Output fields have not been documented yet.

Additional notes

Strale's capability description describes algorithmic token and cost estimates for language-model text. Review the actual response for the model and estimation details that apply to your request.

Keep the submitted text unchanged when comparing estimates. A model name such as gpt-4o is a requested choice, not proof that a particular counting method was used. If the response includes a cost estimate, retain its currency and calculation basis; that estimate does not establish either a provider's bill or the Toolmu charge.

Token counter for LLM text

Token counter for LLM text

You receive

Start with your task

What to know

Inputs & outputs

Input schema

Result schema

Connect the AI you use

Share the text

Understand the estimate