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suno_generate_lyrics

Generate structured song lyrics with section markers from a text prompt. Create lyrics for any theme, mood, or genre to use with music generation.

Instructions

Generate song lyrics from a text prompt.

Creates structured lyrics with proper song sections (Verse, Chorus, Bridge, etc.)
based on your description. The generated lyrics are formatted and ready to use
with suno_generate_custom_music.

Use this when:
- You need lyrics but don't want to write them yourself
- You want AI-generated lyrics for a specific theme or mood
- You need properly structured lyrics with section markers

The output includes section markers like [Verse], [Chorus], [Bridge] that
Suno's music generation understands.

Returns:
    Generated lyrics with title, status, and formatted text with section markers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesDescription of the lyrics you want. Include theme, mood, genre, and any specific elements. Examples: 'A romantic ballad about lost love and rainy nights', 'An upbeat pop song about summer vacation', 'A motivational hip-hop track about overcoming obstacles'
modelNoModel version for lyrics generation. 'default' works well for most lyrics. 'remi-v1' offers an alternative style.default

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that lyrics are generated with section markers and that the output includes title, status, and text. It does not mention side effects, auth needs, or rate limits, but the core behavior is clear.

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?

The description is front-loaded with the main purpose and uses bullet points for usage guidelines. It is reasonably concise with each sentence adding value, though it could be slightly tighter 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?

With a simple two-parameter tool and an output schema present, the description covers input, output, and usage effectively. It lacks error conditions or prerequisites but is sufficient for a straightforward generation task.

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 description coverage is 100%, so the schema already documents both parameters adequately. The description adds little beyond repeating the prompt's role and model options, resulting in minimal additional value.

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 that the tool generates song lyrics from a text prompt, specifying the output includes structured sections. It mentions integration with suno_generate_custom_music but does not explicitly distinguish from sibling tools like suno_mashup_lyrics, so purposeful but lacking full differentiation.

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?

The description provides bullet points for when to use the tool (need lyrics, AI-generated for themes, structured output). However, it does not specify when not to use it or mention alternative tools, leaving some ambiguity.

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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