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Galatic.ai

by ai.galatic
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Separate stems

separate_stems

Split a track into vocals and instruments. Spends Galatic.AI credits. Returns a job id; poll get_job_status with kind "stems".

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": {
      "context": "<context>",
      "llm_model": "<llm_model>"
    },
    "name": "separate_stems"
  }
}

Input Schema

NameRequiredDescriptionDefault
modeNo
taskIdNo
audioIdNo
contextYesExplain 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."
audioUrlNo
llm_modelYesThe 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.
conversation_idNoPass 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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations declare a non-read-only, non-destructive, non-idempotent, closed-world mutation, and the description adds context the annotations do not: the tool is asynchronous, returns a job id rather than a result, requires polling, and costs Galatic.AI credits. These are real behavioral traits beyond the structured fields, though it omits whether the operation is reversible or how long jobs typically take.

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?

Three short sentences, front-loaded with the core action and followed by cost and async-flow details. Every sentence carries distinct, non-redundant information.

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?

For an async, credit-consuming job tool with no output schema, the description adequately covers the call-then-poll lifecycle and cost. What it leaves out is how the input parameters interact (mode/taskId/audioId/audioUrl), which is the main thing an agent still has to guess.

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

Parameters2/5

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

Only three of seven parameters carry schema descriptions (43% coverage), and the description says nothing about the ambiguous ones — mode, taskId, audioId, audioUrl — including how to choose between supplying an audioId versus an audioUrl. With coverage below 50%, the description was expected to compensate and does not.

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 and resource ('Split a track into vocals and instruments'), which is precise enough to distinguish it from siblings like generate_music, remix_music, and generate_voice. There is no ambiguity about what the tool produces.

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

Usage Guidelines3/5

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

It states the downstream workflow ('poll get_job_status with kind "stems"') and warns that credits are consumed, which is genuinely useful. However, it never says when to choose this over alternatives like analyze_media or remix_music, nor does it state prerequisites or exclusions, so the when-to-use guidance is only implied.

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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