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loudness_normalize

Match audio loudness to target LUFS levels for streaming platform standards, ensuring consistent perceived volume.

Instructions

Normalize audio to a target perceived loudness in LUFS.

DANGER — READ BEFORE USING: This tool can DESTROY audio if used incorrectly. It boosts OR reduces audio to hit the target LUFS. On quiet or poorly recorded audio, it may boost by 20-30 dB, causing severe clipping and distortion that ruins the file.

DO NOT use this tool:

  • Right after a pipeline (pipelines already handle loudness safely)

  • On raw/unprocessed audio (clean it up first)

  • Without first running auto_analyze_audio to check current levels

  • If the audio peaks below -20 dB (it's too quiet — use normalize first to gently raise levels)

ONLY use this tool when the user EXPLICITLY asks for LUFS normalization AND the audio has already been processed and has healthy levels (peaks between -6 dB and -1 dB).

Targets (only use these values):

  • -16 LUFS: Podcast, broadcast, Apple Music

  • -14 LUFS: Spotify, YouTube, most streaming

  • -11 LUFS: Loud masters (hip-hop/EDM)

Args: lufs_level: Target loudness in LUFS (-50 to -5). Default: -16.0 stereo_independent: Normalize L/R channels independently. Default: False dual_mono: Treat mono as dual-mono for correct LUFS measurement. Default: True

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dual_monoNo
lufs_levelNo
stereo_independentNo
Behavior4/5

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

Discloses danger: 'This tool can DESTROY audio if used incorrectly. It boosts OR reduces audio to hit the target LUFS. On quiet audio, it may boost by 20-30 dB, causing severe clipping.' No annotations, so description carries full burden; misses some side effects like reversibility.

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 purpose, then structured sections (DANGER, DO NOT, ONLY, Targets, Args). Slightly verbose but every sentence adds value; could be slightly more concise.

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?

No output schema, so description should clarify return value (e.g., modifies selected audio, returns success). Missing what happens after normalization. Adequate but not complete.

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 has 0% description coverage, but description explains each parameter: lufs_level target range and default, stereo_independent behavior, dual_mono usage. Adds meaning beyond type and default.

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?

Clear verb and resource: 'Normalize audio to a target perceived loudness in LUFS.' Distinguishes from sibling tools like normalize, effect_amplify, limiter by specifying LUFS normalization.

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?

Provides explicit when-to-use and when-not-to-use: 'DO NOT use this tool right after a pipeline...', 'ONLY use this tool when the user EXPLICITLY asks for LUFS normalization...' Also lists safe target LUFS values for various platforms.

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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