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generate_sfx

Generate game sound effects from text descriptions using Suno V5. Supports UI clicks, magic spells, item pickups, and explosions; async mode for long generations.

Instructions

Generate a sound effect from text via Suno V5 (kie.ai removed the ElevenLabs sound-effect model). Great for game sounds: UI clicks, magic spells, item pickups, explosions. For loop/BPM/key control use generate_sounds instead. Downloads to kie/assets/raw/.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesSound description (e.g. "magical sparkle chime, fairy-like, short 0.5s")
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.
filenameNoOutput filename. Auto-generated if omitted.
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.
duration_secondsNoTarget duration hint, folded into the prompt (Suno has no hard duration control).
max_wait_secondsNoOverride the blocking-mode polling budget in seconds (defaults: image 600, video 900, audio 300, speech 300). Ignored when wait=false.
prompt_influenceNoDeprecated — ignored (no Suno equivalent). Kept for backward compatibility.
Behavior3/5

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

Discloses the underlying model (Suno V5) and removal of ElevenLabs model, mentions output directory (kie/assets/raw/), and via parameter descriptions explains async mode. However, with no annotations, the description does not fully cover safety, permissions, or guarantees. Provides some behavioral context but not comprehensive.

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?

Extremely concise: three sentences. Front-loaded with core action, followed by examples and a sibling distinction. No wasted words.

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

Completeness3/5

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

Describes model, use case, and alternative, but omits the return value/format (no output schema). While parameter descriptions explain async polling, the description lacks an overall picture of what the agent receives upon completion.

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. The description adds value by suggesting use cases (game sounds) and directing to alternatives, but does not significantly augment the schema's per-parameter meaning.

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 it generates a sound effect from text via Suno V5, with specific examples (game sounds: UI clicks, magic spells, etc.). Explicitly distinguishes from sibling generate_sounds for loop/BPM/key control.

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 a clear when-to-use scenario (game sounds) and an explicit alternative (generate_sounds for loop/BPM/key control). However, lacks guidance on when not to use compared to other sound generation siblings (e.g., generate_tts, generate_dialogue).

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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