Zero-shot vs Few-shot Ratio Calculator

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

A+

Quantify prompt composition by measuring the density of few-shot examples against instructions.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner

This MCP server provides precise analytical tools to determine the prompting strategy used in LLM inputs. By identifying few-shot demarcations like 'Input:' or 'Output:', it calculates the exact ratio of example content to task instructions. Use analyze_prompt_composition to classify a prompt as Zero-shot, Few-shot, or Heavy Few-shot, or use get_demarcation_metrics to count specific markers. It is an essential tool for prompt engineers looking to optimize context usage and density.

prompt-analysisfew-shotzero-shotllm-metricscontext-optimization

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

analyze_prompt_composition

Performs the primary structural analysis of a text string to determine its prompting strategy

compare_prompt_strategies

Compares two different prompt structures to identify which is more "dense" with examples

get_demarcationmetrics

Provides a granular count of specific pattern occurrences to verify the presence of specific few-shot markers

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

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

The prompt is classified as Few-shot with an example-to-instruction ratio of 1.0.

What is the composition of: 'Summarize this text: [long text]'?

This is a Zero-shot prompt as no demarcations were found.

Compare the density of 'Task: A. Input: B. Output: C.' and 'Task: D.'

The first prompt has a higher example density.

The classification identifies if your prompt is Zero-shot (no examples), Few-shot, or Heavy Few-shot (high density of examples) based on the character ratio and demarcation count.

Related Connectors