Normalise loudness

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

The description discloses the two-pass processing behavior and its accuracy tradeoff compared to a single streaming pass, which adds context beyond the annotations (which are all false). It does not mention side effects like file overwriting or permissions, but the output_path schema covers output behavior. Given the sparse annotations, this moderate disclosure is acceptable.

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 compact and well-structured: an opening purpose statement, a brief note on the two-pass behavior, and a final line with target-level recommendations. Every sentence adds value, and the most important information is front-loaded. No redundant or verbose content.

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?

Given the tool's complexity (7 top-level parameters plus an encoder object) and that an output schema exists, the description covers the core function and key behavioral nuance but omits any guidance on advanced parameters (true_peak, loudness_range, encode) or how they interact. It is sufficient for basic use but not fully complete for an AI agent needing to make informed parameter choices.

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 description coverage is 57%, leaving parameters like true_peak and loudness_range undocumented. The description does not help clarify these, and it only repeats the target_lufs guidance already present in the schema (e.g., -16 vs -23 LUFS). For parameters lacking schema descriptions, the description provides no additional meaning, so it fails to fill the gaps.

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 function: 'Normalise a file's loudness to a target level (EBU R128).' It uses a specific verb and resource, and the reference to EBU R128 distinguishes it from generic audio tools. This unambiguously differentiates it from siblings like measure_audio or mix_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 useful context for parameter selection ('-16 LUFS suits online video, -23 LUFS is the broadcast standard') and implies the tool is used when loudness normalization is required. However, it does not explicitly mention when to use this tool over alternatives (e.g., measure_audio) or state any exclusions, so guidance is mostly implied rather than explicit.

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