MCP For Claude Marketplace
Zero-Shot vs Few-Shot Ratio Calculator

Zero-Shot vs Few-Shot Ratio Calculator MCP Connector for Claude

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Analyze prompt structures to classify learning approaches and evaluate example density.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

The Zero-Shot vs Few-Shot Ratio Calculator is a diagnostic tool designed for developers and prompt engineers to understand the structural composition of LLM prompts. By analyzing text patterns, it identifies whether a prompt uses a Zero-Shot, Few-Shot, or Multi-Shot approach based on specific demarcation tokens like 'Example:', 'Input:', or 'Assistant:'.

This MCP server provides deep insights into prompt efficiency by calculating the character weight ratio between instructions and demonstrations. Use classify_prompt_type to determine the learning tier, calculate_composition_metrics to measure instruction-to-example density, and audit_demarcation_usage to audit the frequency of specific anchor tokens. It is an essential tool for optimizing context window usage and ensuring prompt stability across different models.

prompt-engineeringllmzero-shotfew-shotmetricsanalysis

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

audit_demarcation_usage

g., "Example:", "Input:") are present and how often they occur. Provides a detailed census of which specific anchor tokens are being used to structure the prompt

calculate_composition_metrics

Calculates the character weight and ratio between instruction text and example text

classify_prompt_type

Determines if a provided prompt string should be classified as Zero-Shot, Few-Shot, or Multi-Shot based on the presence of demonstrations

See how to talk to your AI agent using Zero-Shot vs Few-Shot Ratio Calculator.

Analyze this prompt: 'Translate the following to French. Input: Hello. Output: Bonjour.'

The prompt is classified as Few-Shot because it contains the 'Input:' and 'Output:' delimiters.

What is the ratio for: 'Summarize this text. Example: Text: Long text... Output: Short summary.'

The tool calculates the character count for the instruction zone and the example zone to provide the exact ratio.

Check the usage of delimiters in: 'User: Hi. Assistant: Hello.'

The audit identifies the presence of 'User:' and 'Assistant:' tokens.

Zero-Shot prompting provides only instructions without any examples, while Few-Shot prompting includes demonstrations (input-output pairs) to guide the model's behavior.

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