Measure loudness

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

With all annotations false, the description carries the burden. It discloses that the operation is a 'job rather than an instant answer' and explains why (integrated loudness cannot be sampled), which is valuable behavioral context. It doesn't mention side effects or permissions, but the async nature is the most important trait here.

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?

Three sentences, front-loaded with purpose, then usage, then behavioral caveat. No filler, every sentence contributes.

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?

Combined with the existence of an output schema and sibling job tools, the description covers purpose, usage, and async behavior. It doesn't spell out how to retrieve results, but the job hint and sibling names (job_status, job_result) provide enough context for an agent to proceed.

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% and the description adds no meaning for input_path beyond 'file.' The tool name and description imply an audio file path, but there is no detail about path types, supported formats, or required conditions. This is a gap for a single-parameter tool.

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 opens with a specific verb+resource: 'Measure a file's loudness' and enumerates concrete metrics (mean, peak, EBU R128 LUFS). This clearly distinguishes it from siblings like normalize_audio or probe_media, which have different purposes.

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 gives concrete usage scenarios: 'decide whether audio is worth keeping' and 'check what a mix or a normalisation actually did.' It doesn't name an alternative tool explicitly, but the examples imply contrast with normalization and other processing tools, so it provides clear context without formal exclusions.

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