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Tokenization Normalizer

Tokenization Normalizer MCP Connector for Claude

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Resolves tokenization drift by normalizing text to match specific LLM tokenizer profiles.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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This MCP server provides tools to resolve tokenization drift, ensuring deterministic control planes can accurately predict LLM behavior. By applying strict transformation rules for specific profiles like cl100k_base or llama, it adjusts whitespace, merges contractions, and normalizes Unicode. Use normalize_text_structure to transform text, analyze_tokenization_drift to assess instability, or get_tokenizer_capabilities to inspect supported rules.

tokenizationllmnormalizationdrift-detectiondeterministic

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

get_tokenizer_capabilities

Informs the user about what specific normalization rules are available for a given tokenizer profile

analyze_tokenization_drift

Compares the original text against a target profile to provide a summary of how "unstable" the text is for that specific model

normalize_text_structure

Transforms a given string to match the structural expectations of a specific target tokenizer

See how to talk to your AI agent using Tokenization Normalizer.

How much drift is in this text for the llama tokenizer: 'Hello, world!'?

The drift severity for 'Hello, world!' using the llama profile is low, with a primary transformation needed in whitespace alignment.

Normalize this text for cl100k_base: 'Don't stop now.'

The normalized text is 'Don't stop now.' and the estimated token count delta is 0.

What rules does the llama tokenizer support?

The llama tokenizer supports unicode_standardization and contraction_merging rules.

Tokenization drift occurs when the same text results in different token counts or boundaries across different LLM models. This tool helps align text to a target profile to minimize these differences.

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