Prompt Token Analyzer

Prompt Token Analyzer MCP Connector for Claude

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Deterministic token breakdown and budget analysis for prompt engineering.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner

This MCP server provides precise tools for managing token budgets in LLM workflows. It uses a deterministic 4-character-per-token ratio to calculate consumption across different prompt segments. Use analyze_prompt_sections for a detailed breakdown of tokens and budget adherence, get_section_stats for high-level summaries, or find_outlier_sections to identify bloated or empty sections. It helps developers optimize context windows and control costs effectively.

tokensbudgetprompt-engineeringllmoptimization

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

analyze_prompt_sections

Performs a full breakdown of a prompt's token consumption and budget adherence

find_outlier_sections

Identifies sections that are significantly larger or smaller than the average section size

get_section_stats

Provides high-level summary statistics about the prompt's composition

See how to talk to your AI agent using Prompt Token Analyzer.

Analyze these prompt sections: 'System: You are a helpful assistant' (35 chars) and 'User: Hello' (11 chars) with a budget of 20 tokens.

The total token count is 11.5 tokens. The budget remaining is 8.5 tokens. The largest section is 'System: You are a helpful assistant'.

Give me a summary of the token usage for these sections: 'Instruction' (12 chars) and 'Data' (100 chars) with a budget of 50 tokens.

Total tokens: 28. Budget remaining: 22. The system is within budget.

Find outliers in these sections: 'A' (1 char), 'B' (1 char), 'C' (1 char), and 'D' (100 chars).

The large outlier is 'D'.

The server uses a deterministic proxy where the token count is the total number of characters in a section divided by four.

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