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video_normalize_audio

Normalize video audio loudness to a target LUFS level for compliance with platform loudness standards such as YouTube (-16 LUFS) or broadcast (-23 LUFS).

Instructions

Normalize audio loudness to a target LUFS level.

Common presets: -16 (YouTube), -23 (EBU R128/broadcast), -14 (Apple/Spotify).

Args: input_path: Absolute path to the input video. target_lufs: Target integrated loudness in LUFS (default -16 for YouTube). output_path: Where to save the output. Auto-generated if omitted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
input_pathYes
target_lufsNo
output_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It mentions output_path auto-generation but does not specify whether the operation is destructive, modifies the input, or other side effects. Minimal transparency.

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?

The description is concise with no redundant sentences. It front-loads the purpose, lists presets, and explains parameters efficiently.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 3 parameters and no annotations, the description covers the core functionality. However, it lacks mention of output format or limitations. The existence of an output schema partially compensates, but more completeness would be beneficial.

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 description coverage is 0%, but the description explains input_path, target_lufs with default and presets, and output_path auto-generation. This adds meaningful context beyond the schema, though more detail on format restrictions could improve.

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 clearly states it normalizes audio loudness to a target LUFS level, which is a specific verb and resource. It also lists common presets, distinguishing it from other audio tools like audio_effects or audio_preset.

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?

The description implies usage for audio normalization but does not provide explicit guidance on when to use this tool versus alternatives like audio_effects or audio_preset. No when-to-use or when-not-to-use information is given.

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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