AetherWave Studio

Master an audio track (AI mastering)

aetherwave_master_audio

Submits an audio file for AI mastering and returns the mastered URL (the route polls the Python service internally; a typical track takes 30-90 s, a long one up to 5 min). Useful as a final polish step after music generation. Cost: 20 credits per track. Producer, Mogul, and Ultimate plans get mastering free. Output is WAV (~50MB per 3-minute track, lossless for redistribution). Pick a preset to steer the mastering style; call aetherwave_list_master_presets for the full live list (12 presets including streaming, loud, gentle, hip_hop, edm, pop, rock, lofi, rnb, acoustic, cinematic, podcast). Each preset has a target LUFS value so you can match the distribution target.

Returns within ~45 s. This pipeline has NO job id (the request itself is the job), so if the result has state "running" the master is still finishing server-side and will be saved to the gallery: find it with aetherwave_list_my_creations type "audio" (newest first) rather than resubmitting, which would master and charge it again. Pass wait:true ONLY if your MCP client permits tool calls longer than 60 s.

Input Schema

TableJSON Schema

Name	Required	Description
`wait`	No	Block until the job finishes (up to the tool's full budget, 6-15 minutes). ONLY if your MCP client permits tool calls longer than 60 s; most abandon the call at 60 s and lose the taskId. Default false: return within ~45 s and hand back a taskId if still running.
`preset`	Yes	Mastering preset name. Must be one of: 'streaming', 'loud', 'gentle', 'hip_hop', 'edm', 'pop', 'rock', 'lofi', 'rnb', 'acoustic', 'cinematic', 'podcast'. Call aetherwave_list_master_presets for full metadata (target LUFS, description, tags).
`audioUrl`	Yes	Public URL to the source audio file (MP3 or WAV).
`trackTitle`	No	Optional title for the mastered output (used in gallery row label).

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 1, 2026

addedInput schema / properties / wait

Added value: +{
+  "description": "Block until the job finishes (up to the tool's full budget, 6-15 minutes). ONLY if your MCP client permits tool calls longer than 60 s; most abandon the call at 60 s and lose the taskId. Default false: return within ~45 s and hand back a taskId if still running.",
+  "type": "boolean"
+}

First observedJun 20, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Goes well beyond the annotations with credit cost (20 credits, free on Producer/Mogul/Ultimate), latency (30-90 s, up to 5 min), output format and size (WAV, ~50MB per 3 min), and the polling/no-job-id behavior. The only blemish is an internal inconsistency: the description says 'this pipeline has NO job id' while the schema says the call 'hand[s] back a taskId if still running' — an agent could be confused about how to recover a running result.

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 return value, then cost, latency, and recovery guidance. It is information-dense rather than padded, though the 12-preset list duplicates the schema enum and the two latency figures ('~45 s' vs '30-90 s') slightly muddy the message.

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?

With no output schema, the description carries the full burden and does so: it describes the returned mastered URL, the 'running' state, where the finished file lands (gallery), and the cost model. Nothing an agent needs to invoke it correctly is missing.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning beyond the schema: it enumerates the 12 live presets, notes each carries a target LUFS so the agent can match a distribution target, and explains the wait:true trade-off in prose. Marginal value above the schema, but genuine.

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?

States a specific verb and resource ('submits an audio file for AI mastering') and names the artifact returned ('returns the mastered URL'). This is clearly distinguishable from siblings like aetherwave_generate_music (creation) and aetherwave_get_job (polling).

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

Usage Guidelines5/5

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

Explicitly positions the tool ('useful as a final polish step after music generation') and routes the agent to alternatives: aetherwave_list_master_presets for preset metadata and aetherwave_list_my_creations for retrieving a still-running result instead of resubmitting. It also states a when-not condition ('do not resubmit, which would master and charge it again').

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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