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by io.github.molkex
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music_generate

Generate a full song from a text brief via Google Flow Music (Lyria).

prompt — free text: genre, mood, BPM, instruments, and whether you want vocals or an
instrumental (e.g. "dreamy synthwave, retro 80s, 110 bpm, instrumental"). The Producer
agent turns it into a finished track (usually ~2-3 minutes, and it returns TWO variants).

model — "lyria" (Lyria 3.5, the default flagship) or "lyria-pro" (Lyria 3 Pro, legacy).
lyrics — optional explicit words to sing; omit for the model to write its own / stay
instrumental.

SAVE THE RESULT. Each returned clip carries a public `audio_url` (a Google Cloud Storage
.m4a, no auth header needed) plus `download_path` on our server. Unless the user said
otherwise, download the audio into their project. Fields per clip: clip_id, title,
duration (seconds), lyrics, audio_url, wav_url, image_id (cover, feeds music_video),
image_url. Returns {"clips": [...], "media": [...], "conversation_id": "..."} when done,
or a job_id to poll with check_job if it isn't ready within ~90s.

conversation_id — pass the one from an earlier result to keep working on the same track
("same song but 30 seconds shorter"); the Producer remembers what it just made. Omit it
for a fresh start. Every result carries one, so a follow-up is always possible.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNolyria
lyricsNo
promptYes
conversation_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only indicate readOnly=false and idempotent=false; the description adds crucial behavioral detail: the tool returns two variants, may become async with a job_id, produces ~2-3 minute tracks, and instructs the agent to save the result. It also discloses the public audio_url access model and the full set of returned clip fields, going well beyond annotation signals.

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?

The description is dense but every sentence carries operational value, from the prompt brief to the output schema to the conversation_id follow-up. It is front-loaded with the core purpose and organized by parameter and result-handling concerns, with no filler.

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

Completeness5/5

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

The tool is complex: 4 parameters, no output schema, async behavior, and a large sibling set. The description provides the output JSON shape, field semantics, auth details, download guidance, and follow-up mechanism, making it complete for an agent to invoke and process the result correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description fully compensates by explaining every parameter: prompt with an illustrative example, model with enum values and legacy status, lyrics with omission semantics, and conversation_id with follow-up behavior. It adds practical meaning that the raw input schema does not convey.

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?

The description opens with a specific verb, resource, and mechanism: 'Generate a full song from a text brief via Google Flow Music (Lyria).' It clearly distinguishes this from siblings like music_chat and music_stems by focusing on generation and the returned two variants. The scope and output expectation are unmistakable.

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?

It provides clear when-to-use context: pass conversation_id to continue from an earlier track and poll with check_job if the result is not ready. It does not explicitly list exclusions or name sibling alternatives, but the generation-vs-chat/download distinction is clear enough from the content.

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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