loudness_normalize

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

With no annotations, the description carries the transparency burden and discloses a major risk: it can boost quiet audio by 20-30dB and cause clipping, and instructs to measure first. It does not explicitly mention irreversibility or whether the operation applies to a selection, but the warning is concrete and useful.

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, front-loaded with the action, then the danger warning and practical targets, then a clean Args block. Every sentence adds necessary information; there is no filler.

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 low-complexity tool with no required params and no output schema, the description covers the operation, risks, usage scenarios, and all parameters. The only notable gap is that it does not explicitly state what audio is affected (selection, track, or whole project), which an agent may need to infer from context.

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 0%, but the description compensates fully by documenting all three parameters with units, ranges, defaults, and behavioral meaning (e.g., lufs_level range -145 to 0, stereo_independent semantics, dual_mono purpose). This adds value the schema does not provide.

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 names a specific verb and resource ('Normalize audio to a target LUFS loudness') and distinguishes itself from the generic sibling 'normalize' by the LUFS target and platform-specific values. Even without an explicit sibling comparison, the purpose is unambiguous.

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?

It provides clear usage context: recommended target LUFS values per platform and an explicit prerequisite to measure levels with auto_analyze_audio first. It does not explicitly state when to prefer normalize or other effect tools, so it stops 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.