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detect_ai_music

Analyze an audio file to determine if it is AI-generated music. Returns a verdict (likely AI-generated, uncertain, or likely human) with probability, confidence, and provenance signals.

Instructions

Detect whether an AUDIO file is AI-generated music.

Pass an absolute path to a local audio file (.mp3, .wav, .m4a, .flac). Returns a verdict (LIKELY AI-GENERATED / UNCERTAIN / LIKELY HUMAN), an AI probability %, confidence, and any provenance signals (Content Credentials / SynthID watermark, generator hints). Note: tuned for MUSIC; a bare synthetic-voice clip with no music may read low.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYes
Behavior4/5

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

No annotations provided, but description fully discloses behavioral traits: returns verdict, probability, confidence, provenance signals. Notes tuning limitation for music. Does not mention error handling or non-destructive nature, but covers key behaviors.

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

Conciseness5/5

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

Three sentences: purpose, input requirements, output and limitation. Every sentence adds value, no redundancy. Front-loaded with primary purpose.

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 no output schema and one parameter, description covers input, output format, and limitation. Sibling tools provide context. Missing error behavior slightly reduces completeness, but largely sufficient for proper invocations.

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

Parameters4/5

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

Single parameter 'file_path' has 0% schema description coverage. Description compensates by specifying absolute path requirement and allowed formats (.mp3, .wav, .m4a, .flac), adding meaningful semantic context beyond the schema.

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

Purpose5/5

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

Clearly states 'Detect whether an AUDIO file is AI-generated music', specifying the verb (detect) and resource (audio file). Differentiates from siblings like detect_ai_media by noting it is 'tuned for MUSIC'.

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

Usage Guidelines4/5

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

Provides explicit usage: 'Pass an absolute path to a local audio file' with supported formats. Includes exclusion note for synthetic-voice clips without music, guiding when not to use. Does not explicitly name alternatives, but sibling context implies detect_ai_media.

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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