Analyze text for AI attribution using Pangram's attribution analysis API.

This tool provides transparency analysis for AI-assisted content, supporting editorial review and quality assurance workflows. It identifies authorship patterns and provides segment-level attribution breakdowns.

Analysis includes:

• Overall authorship assessment

• Average and maximum AI attribution scores

• Breakdown of authorship patterns by segment

• Segment-by-segment attribution analysis with confidence levels

Args:

• text (string): The content to analyze. Must be at least 50 words for accurate analysis.

• response_format ('markdown' | 'json'): Output format (default: 'markdown')

Returns:

• Markdown: Formatted transparency report with authorship assessment and segment analysis

• JSON: Structured data with all attribution metrics

Use cases:

• Editorial review before publication

• Transparency audits for AI-assisted content

• Quality assurance for professional writing workflows

• Attribution documentation for compliance requirements

Requires: PANGRAM_API_KEY environment variable to be set

Get a quick attribution snapshot for editorial review.

Returns essential metrics for fast iteration during the writing and editing process. Use this for rapid checks when detailed segment analysis isn't needed.

Args:

• text (string): The content to analyze. Must be at least 50 words.

Returns: A brief summary with:

• Authorship indicator

• Attribution percentage

• Review status

Example: "Quick snapshot for this paragraph" → "✅ PRIMARY AUTHOR - 12.3% AI attribution"