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Medhā MCP

by vivmuk

Venice Music Queue

venice_music_generate

Generate music by submitting a prompt and selecting a model to queue AI music or sound-effect creation, then check status with the status tool.

Instructions

Queue music generation. Available models: ace-step-15, elevenlabs-music, minimax-music-v2/v25/v26, stable-audio-25, mmaudio-v2-text-to-audio, elevenlabs-sound-effects-v2. Operator preferences — default: ace-step-15; also try: elevenlabs-music / minimax-music-v2 / minimax-music-v25. Operator default = ace-step-15 (broad genre). Elevenlabs-music for vocal. MMaudio-V2 for stem separation. Uncensored: NSFW prompts allowed where the model permits. Supports x402 wallet auth (no Venice account needed) and API key. Returns { model, queue_id }; poll with venice_music_status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesRequired. Music model id, e.g. "elevenlabs-music".
lyricsNo
promptYes
instrumentalNo
duration_secondsNo
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 auth requirements (x402 wallet, API key), NSFW policy, and that it returns {model, queue_id} with polling via venice_music_status. This is meaningful behavioral context, though it doesn't fully describe error behaviors, rate limits, or what happens on failure.

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

Conciseness3/5

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

The description is dense with useful operational details but somewhat rambling, mixing model lists, operator preferences, and auth info in a somewhat unstructured flow. It's informative but could be tidier with clear separation of concerns. The return-value note is valuable and front-loaded reasonably well.

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?

Despite no output schema and minimal schema param coverage, the description covers model selection, auth methods, return format, polling flow, and policy boundaries (NSFW). For a queue-submission tool of moderate complexity, this is reasonably complete for agent guidance. It could add error handling and quota/rate-limit notes but is close to sufficient.

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 only 20%, meaning the schema documents very few parameter meanings. The description compensates somewhat by explaining model selection semantics (which models map to which use cases), but it doesn't add detail on prompt, lyrics, instrumental, or duration_seconds parameters beyond what minimal schema descriptions provide. Some help, not comprehensive.

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 states the tool 'Queue music generation' with a specific verb and resource, clearly indicating this is the queue-submission step for music generation. It distinguishes from siblings like venice_music_status and venice_music_complete by noting the return value and polling flow. However, it doesn't explicitly contrast with venice_music_status/complete within the text, relying on context.

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 model selection guidance including operator preferences (default ace-step-15), use-case-specific recommendations (Elevenlabs for vocal, MMaudio for stem separation), and notes about NSFW and auth paths (x402 wallet, API key). It clearly communicates when this is appropriate though it doesn't explicitly name alternatives or when-not-to-use scenarios beyond the model guidance.

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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