ffmpeg__normalize_audio

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

There are no annotations, so the description carries the full burden. It discloses the output behavior ('Returns JSON in stdout with output_path and applied normalization parameters') and gives defaults, which is useful. However, it does not mention whether the output file is overwritten, what happens if the input is a video vs audio, or error handling, leaving some ambiguity.

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?

The description is concise and well-structured: a single purpose sentence, a remark about output/defaults, and parameter list. Every sentence carries necessary information without redundancy.

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 6-parameter tool with no annotations or output schema, the description covers purpose, all parameters with defaults, return format, and input types (audio or video path). It does not cover edge cases like overwrite behavior or error handling, but overall it is reasonably complete for an ffmpeg audio tool.

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?

The schema has 0% description coverage, but the tool description compensates with a @param line for each of the 6 parameters, including clear explanations and defaults. This fully adds meaning beyond the bare schema types.

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 clearly states the tool's purpose: 'Normalize audio loudness to a target EBU R128 LUFS using the ffmpeg loudnorm filter.' This is a specific verb and resource (loudness normalization) and distinguishes it from sibling ffmpeg tools like mix_audio_tracks or mux_audio.

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?

The description provides context by noting the default target of -14 LUFS is 'suitable for most streaming platforms (YouTube, Spotify),' implying when to use the default. However, it does not explicitly state when not to use this tool or mention alternatives, so it falls short of a 5.

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