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nik-kale
by nik-kale

Attribution Audit

pangram_attribution_audit
Read-onlyIdempotent

Analyze text for AI attribution, providing authorship scores and segment-level breakdowns to support editorial review and transparency audits.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe text content to analyze for AI detection. Minimum 50 words required.
response_formatNoOutput format: 'markdown' for human-readable or 'json' for structured datamarkdown
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds context about requiring an API key, minimum text length, and analysis details, but does not disclose potential limitations or error handling, which would enhance transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is structured with bullet points and sections, but is somewhat verbose and could be more concise. Some information is redundant with the schema, and the description could be trimmed without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has two parameters, no output schema, and annotations, the description covers purpose, parameters, output formats, and use cases adequately. It mentions required environment variable and minimum text length. Could be improved by noting error conditions or rate limits, but overall complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, with both parameters described in the schema. The description reiterates this information and adds a default for response_format, but does not provide meaningful additional semantics beyond what the schema already offers.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool analyzes text for AI attribution using Pangram's API, listing specific outputs and use cases. However, it does not differentiate from the sibling tool 'pangram_quick_snapshot', missing the opportunity to clarify unique purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides several use cases (editorial review, transparency audits, QA, compliance) but does not specify when not to use this tool or suggest alternatives. Given the sibling tool, explicit guidelines for selection would be beneficial.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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