Char-to-Token Estimator

Char-to-Token Estimator MCP Connector for Claude

A+

Predict token usage for different LLMs using language-specific character ratios.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner

This MCP server provides deterministic token estimation by analyzing text density across various languages. It uses specific character-to-token ratios for languages like English, Chinese, and Arabic, and applies model-specific modifiers for GPT, Claude, and Llama architectures. Use estimate_tokens to get a full breakdown of character counts, word counts, and estimated tokens, or get_text_statistics for basic linguistic metrics.

tokensllmtextestimationlinguistics

3 tools expose this connector's capabilities to your AI agent.

get_text_statistics

Provides basic linguistic metrics (characters and words) without performing token estimation

validate_language_support

Checks if a specific language is supported by the estimator

estimate_tokens

Calculates the estimated token count for a given string based on language and model type

See how to talk to your AI agent using Char-to-Token Estimator.

Estimate the tokens for the text 'Hello world' using English and the GPT model.

The text 'Hello world' has 11 characters and 2 words. The estimated token count for GPT is 3.

How many tokens would '你好' use in a Claude model?

The text '你好' has 2 characters and 1 word. The estimated token count for Claude is 2.

Get the word count and average characters per word for 'This is a test.'

The text has 4 words and an average of 3.25 characters per word.

The tool uses a deterministic character-density approach with a fixed confidence level of 0.85, providing a reliable approximation for various LLM architectures.

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