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loudness_normalize

Normalize audio to target loudness standards for streaming platforms using LUFS measurement. Adjust perceived volume to meet podcast, broadcast, or music streaming requirements while preventing clipping and distortion.

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
lufs_levelNo
stereo_independentNo
dual_monoNo
Behavior5/5

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

No annotations provided, so description carries full disclosure burden. 'DANGER' header and 'DESTROY audio' warning clearly mark destructiveness. Explains specific failure modes: 'boost by 20-30 dB, causing severe clipping and distortion.' Documents output targets (-16, -14, -11 LUFS) with domain-specific use cases (Podcast, Spotify, hip-hop).

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?

Lengthy but well-structured with clear visual hierarchy (DANGER header, bullet points for targets). Every section serves safety or operational clarity. Slight verbosity justified by destructive nature of tool, though 'Args:' section could be tighter.

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?

Comprehensive for a destructive audio tool with no output schema. Covers: purpose, safety risks, prerequisites (auto_analyze_audio), sibling tool relationships, valid parameter ranges, and target standards. No gaps in operational context.

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 (only titles). Description fully compensates via 'Args:' section: adds semantic meaning ('Target loudness in LUFS'), valid ranges (-50 to -5), and behavioral context ('Normalize L/R channels independently', 'Treat mono as dual-mono') that schema titles lack.

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?

Opening sentence 'Normalize audio to a target perceived loudness in LUFS' provides specific verb (normalize), resource (audio), and metric (LUFS). Clearly distinguishes from sibling tool 'normalize' (implied to be peak normalization) by specifying LUFS targeting.

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?

Exceptional guidance with explicit 'DO NOT use' and 'ONLY use' sections. Names specific alternatives: 'use normalize first' for quiet audio, 'running auto_analyze_audio to check current levels' as prerequisite, and warns against pipeline use ('pipelines already handle loudness safely'). Clear prerequisites (peaks between -6 dB and -1 dB).

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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