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Master a track

master_track

Process one audio track for release to streaming targets: apply presets, noise reduction, watermark cleanup, and dynamics restoration; returns a job ID for polling.

Instructions

Master one track. Uses 1 CREDIT when the account has a balance; otherwise it runs on the free quota (1 free master a day, without advanced modules). Returns a job id immediately - poll get_job (typical 20-60 s, up to 10 min with restoration). The source is fetched by this server, so the ceiling here is 100 MB per file. Pass dry_run=true first if you want to see what would be used and the effective settings before anything is spent.

Input Schema

TableJSON Schema

Name	Required	Description
`style`	No	Preset key from list_presets, e.g. edm, hiphop, podcast, unlimiter. Default standard.
`bitrate`	No	MP3: 128/192/256/320; OPUS: 128/192.
`dry_run`	No	Do not master: report what this call would use and what settings would apply. Nothing is charged, the file is not even downloaded.
`audio_url`	Yes	Public https URL of the audio file (wav/mp3/flac/m4a). A share link to the page works too — Suno/Udio song pages, Google Drive and Dropbox links are resolved to the file automatically.
`intensity`	No	Multiplier on the preset's multiband compression: 1.0 as designed, 0.5 gentler, 1.5 denser. Values outside 0.5-1.5 are clamped.
`out_format`	No
`dither_type`	No	Dither for 16-bit WAV output: tpdf (default), ns_e or ns_itu (noise-shaped).
`target_lufs`	No	Override the preset target, e.g. -14 for Spotify.
`webhook_url`	No	Public https URL to POST the result to instead of polling.
`denoise_strength`	No	Noise reduction, 0-1. 0 disables.
`remove_watermark`	No	Also run AI artifact cleanup first. Free.
`restore_dynamics`	No	Open up a brickwalled source before mastering (pairs with style unlimiter).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`job_id`	No
`dry_run`	No
`poll_with`	No
`would_charge`	No
`tokens_balance`	No
`nothing_was_charged`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 15, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Goes well past the annotations (readOnlyHint=false, destructiveHint=false, openWorldHint=true) by disclosing the credit cost model, the free-quota fallback (1 free master/day without advanced modules), the 100 MB server-side fetch ceiling, and the asynchronous nature (job id returned, 20-60 s typical, up to 10 min with restoration). These are exactly the behavioral facts an agent needs before spending money.

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?

Five sentences with no filler, front-loaded on purpose and then cost, then the async/polling contract, then the size limit, then the dry_run recommendation. Every sentence carries information; the ordering is logical and easy to scan.

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?

For a 12-parameter, paid, asynchronous tool with an output schema, the description covers the essentials an agent needs: billing behavior, quota fallback, source fetching and size cap, and the polling/return contract. The output schema carries the return shape, so nothing critical is missing.

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 92%, so the schema already documents nearly every parameter, including dry_run, intensity clamping, bitrate sets, and audio_url resolution. The description restates dry_run's no-charge behavior and adds the 100 MB ceiling, but contributes little parameter meaning the schema does not already carry. Baseline 3 applies when the schema does the heavy lifting.

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+resource ('Master one track') and immediately scopes it to a single track, which cleanly separates it from the batch_master sibling. The first sentence is enough for an agent to identify the operation without opening the schema.

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 clear workflow guidance: pass dry_run=true first to preview cost and settings, poll get_job after submitting, or supply webhook_url instead. It names the get_job sibling explicitly. It stops short of saying when to prefer batch_master or preview_styles over this tool, so it is clear context rather than a full alternative matrix.

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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