analyze_label_sounds

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

With no annotations provided, the description carries the full burden, and it delivers remarkably thorough disclosure. It explicitly warns that measurement, label_type, pre_offset, post_offset, and label_text are 'not yet independently live-tested against a running Audacity — verified against the scripting reference and unit tests with mocked responses only.' This is excellent transparency about tool reliability. It also distinguishes which parameters are trusted (threshold_db/min_silence_duration/min_sound_duration) from those that aren't.

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 well-structured with a clear opening sentence, a practical usage paragraph, a crucial caveat about untested parameters, and a clean Args section. It's front-loaded with the purpose. The main deduction is that the parameter list duplicates the schema defaults verbatim, adding some redundancy — though the added semantics justify most of it.

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 tool with 8 parameters, 0% schema coverage, no outputs schema, and no annotations, this is remarkable. It explains what the tool does, provides a concrete use case, warns about untested functionality, and documents every parameter. The only missing element is return-value behavior, but with no output schema and the core purpose being a labeling side-effect, this is acceptable.

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?

With 0% schema description coverage and no enums, the description compensates heavily. It provides one-line explanations for all 8 parameters, including units (dB, seconds), valid values for measurement (peak, avg, rms), and semantic detail for label_type (before, after, around, between). The only gap: enum values are listed but not formally constrained in the schema, and no guidance on interaction between params. Still, this is strong compensation.

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 'Automatically label regions of sound separated by silence' and mentions use of label_type='between' for labeling silences. It has a specific verb+resource (label sound regions) and distinguishes itself from siblings by focusing on automated sound/silence detection, differentiating it from manual label tools like label_add or label_regular_intervals. It does not explicitly name a sibling alternative, so not a 5.

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 explicitly frames this as 'A good starting point for segmenting a long recording' and explains a specific workflow: use label_type='between' to label silences 'so they can be trimmed with label_delete_regions.' This provides clear usage context and a concrete workflow suggestion. However, it doesn't explicitly state when NOT to use it versus alternatives like analyze_beat_finder or truncate_silence, so a 4 rather than 5.

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