loudness

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

With no annotations, the description carries the full behavioral burden. It usefully discloses the algorithm ('single-pass loudnorm', implying a faster but less precise one-pass normalization) and the default target, but says nothing about overwrite behavior on the output path, re-encoding/codec consequences, or whether the operation is destructive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences; the operation and its default are front-loaded, and the parenthetical reference value is genuinely informative rather than filler. Nothing needs trimming.

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

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

For a four-parameter audio-mutation tool with no annotations and no output schema, the definition covers the core operation but omits output-path semantics, format handling, and any indication of what happens to the source file. Adequate to select the tool, thin for invoking it confidently.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 25%, so the burden to explain parameters falls largely on the description. It does add real meaning for target_lufs by naming the -14 LUFS YouTube/Spotify convention, but leaves 'format' and especially 'output' (a $ref to the input path definition) unexplained, including whether the output must differ from the input.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb and resource ('Normalize loudness') plus the concrete mechanism ('single-pass loudnorm filter'), so an agent knows exactly what operation runs. It doesn't reference any sibling, but no sibling in the list (concat, convert, trim, etc.) overlaps with loudness normalization, so ambiguity risk is low.

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

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

'Good for speech and creator audio' gives an implied when-to-use signal, but there is no explicit when-not guidance, no prerequisite (e.g. source must be audio-only or have an audio track), and no named alternative for other normalization goals.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.