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suno_mashup_lyrics

Combine two song lyrics into a cohesive mashup for AI music generation. Blend verses and choruses from different sources to create unified lyrical compositions ready for production.

Instructions

Generate mashup lyrics by combining two sets of lyrics.

Takes two different song lyrics and intelligently combines them into
a cohesive mashup. Great for creating unique lyrical combinations.

Use this when:
- You want to blend two songs' lyrics together
- You're creating a mashup or medley
- You want creative lyrical combinations from two sources

Returns:
    Combined mashup lyrics ready for use in music generation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lyrics_aYesThe first set of lyrics to combine. Can be full song lyrics with section markers.
lyrics_bYesThe second set of lyrics to combine. Can be full song lyrics with section markers.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations provided, so description carries full burden. Adds qualitative context ('intelligently combines,' 'cohesive mashup') indicating AI-driven processing. Lacks operational specifics like rate limits, content restrictions, handling of mismatched song lengths, or section marker preservation rules.

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

Conciseness4/5

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

Well-structured with clear sections: mechanism, value proposition, usage scenarios, and return value. Front-loaded with purpose. Minor redundancy between 'Great for creating unique lyrical combinations' and the 'Use this when' bullets, but overall efficient.

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 2 simple string parameters and an output schema, description adequately covers the conceptual model and explicitly mentions return value suitability ('ready for use in music generation'). Slight gap in not clarifying the workflow relationship with `suno_mashup_music` or music generation tools.

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 detailed parameter descriptions (including mention of 'section markers'). Description states 'Takes two different song lyrics' but does not add semantic meaning beyond what the schema already provides, meeting the baseline for high-coverage schemas.

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?

Description opens with specific verb ('Generate') and resource ('mashup lyrics'), clearly stating it combines 'two sets of lyrics.' Explicitly distinguishes from sibling `suno_mashup_music` (audio) and `suno_generate_lyrics` (original generation) by focusing on combining existing lyrical content.

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 'Use this when:' section with three concrete scenarios (blending lyrics, creating mashups/medleys, creative combinations). However, it does not explicitly warn against the likely confusion with sibling `suno_mashup_music` (audio mashup vs. lyrics mashup).

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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