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cover_audio

Transform uploaded audio into AI covers with customizable vocals, style, and instrumentation.

Instructions

Create an AI cover from uploaded audio — custom vocals, style, and instrumentation via Suno.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
waitNoSet false to submit and return immediately with the task_id (async mode) — then poll with check_task and fetch with download_result. Recommended for long generations to avoid client-side watchdog timeouts.
modelNoV5
styleNo
titleNo
promptNoDescription of desired cover style
filenameNo
uploadUrlYesURL of audio to cover
customModeNo
vocalGenderNoVocal gender preference
download_dirNoAbsolute directory to save the file(s) into (created if missing). Defaults to the server's kie/assets/raw/. Must be absolute — the MCP server's working directory is not the caller's.
instrumentalNo
negativeTagsNoTags to avoid in the cover
max_wait_secondsNoOverride the blocking-mode polling budget in seconds (default: audio 300). Ignored when wait=false.
Behavior2/5

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

With no annotations, the description must disclose behavioral traits but only mentions 'via Suno'. It fails to state whether the operation is synchronous/asynchronous, requires specific permissions, or has side effects (e.g., saving files). The wait parameter's behavior is documented in the schema, not the description.

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?

Single sentence is concise and front-loaded, but could include more detail without becoming verbose. Every word has purpose, but the description is under-specified given the tool's complexity.

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

Completeness2/5

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

Given 13 parameters, no output schema, and no annotations, the description is far from complete. It does not explain return values, error handling, or async behavior, which is critical for an AI agent to use the tool correctly.

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 54% (moderate). The description adds minimal semantic value by hinting at parameters like style, vocalGender, and instrumental. However, important parameters like wait, model, prompt, and download_dir are not addressed, leaving gaps that the schema partially covers.

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 clearly states verb 'Create an AI cover' and resource 'from uploaded audio'. Distinguishes from sibling tools like generate_music (generates music from scratch) and generate_sfx (sound effects) by specifying it's a cover with custom vocals, style, and instrumentation via Suno.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool vs alternatives like generate_music or generate_tts. The description does not provide use cases or exclusions, leaving the agent to infer based solely on the tool name and brief purpose.

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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