Galatic.ai

Generate music

generate_music

Generate a song or instrumental. The default engine is Google Lyria (songs with vocals or instrumentals, up to about 3 minutes), cleared for commercial use: Google claims no ownership of the output, but it is not a copyright grant and there is no IP indemnity; it needs the account's one-time 18+ confirmation in Music Studio. model: "lyria" asks for it explicitly; a "suno-..." model gives a Suno draft that is NOT cleared for commercial use. commercial: true marks the music as for YouTube, ads, a client or a release. With a suno model, customMode: true sings prompt as the lyrics: put real lyrics there, or leave customMode off. Spends Galatic.AI credits. Returns a job id; poll get_job_status with kind "music".

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>",
      "context": "<context>",
      "llm_model": "<llm_model>"
    },
    "name": "generate_music"
  }
}

Input Schema

Name	Required	Description
`model`	No
`style`	No
`title`	No
`lyrics`	No
`prompt`	Yes
`context`	Yes	Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`commercial`	No
`customMode`	No
`instrumental`	No
`conversation_id`	No	Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.
`durationSeconds`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 24, 2026

TDQS

A4.2/5.0

Behavior5/5

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

Annotations only cover safety flags; the description adds substantive behavior: credits are spent, the call returns a job id that must be polled via get_job_status with kind 'music', an account-level one-time 18+ confirmation is required, and the licensing/indemnity caveats differ between Lyria and Suno. This is meaningful context beyond structured fields.

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?

Front-loaded with the core action and default engine, then layers model/licensing decisions and the async polling instruction. Dense and long, but nearly every sentence conveys operative detail; a little could be tightened.

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?

For a 12-param, no-output-schema, non-idempotent generation tool, the description covers the async return model, credit cost, auth/prerequisite (18+ confirmation), and licensing distinctions. Remaining gaps are per-parameter details (style, duration, instrumental) rather than workflow-critical omissions.

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

Parameters4/5

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

With only 25% schema coverage across 12 params, the description carries the burden and does so for the key ones: model (lyria/suno names), commercial, customMode (lyrics-singing behavior), and the lyrics-via-prompt nuance. It leaves style, title, lyrics, instrumental, and durationSeconds unaddressed, so not fully complete.

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?

Starts with a specific verb+resource ('Generate a song or instrumental') and goes further by naming the default engine (Google Lyria) and contrasting model behaviors (lyria vs suno). It distinguishes model paths clearly, though it never explicitly positions itself against nearby siblings like remix_music or generate_midi.

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?

Gives concrete selection criteria: use 'lyria' for the cleared default, 'suno-...' for a non-commercial draft, and set customMode only with a suno model to sing lyrics. Provides clear when-to-use context and a licensing fork, but no explicit when-not-to-use or sibling routing.

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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