label_cut_regions

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

With no annotations, the description fully carries the behavioral disclosure burden. It clearly warns that only the last cut survives on the clipboard, explains that regions are processed last-to-first to preserve label timing, and states that the label track is unaffected. This is exemplary transparency for a mutating operation.

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 one-sentence summary followed by critical caveats about clipboard behavior, processing order, and label track preservation. Every sentence adds meaningful information without padding.

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 zero-parameter tool with no output schema, the description covers the operation, side effects, ordering behavior, and what remains unaffected. An agent has enough information to invoke it correctly and anticipate its consequences.

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 tool has zero parameters, so there are no parameter semantics to clarify. Per the baseline for parameterless tools, this is effectively complete; the description need not add anything.

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 and resource: 'Cut the audio under every label to the clipboard, closing the gaps.' This clearly identifies what the tool does and distinguishes it from generic edit_cut or label deletion tools by emphasizing all labeled regions and gap-closing behavior.

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 implies the use case: removing all labeled audio regions and closing the resulting gaps. However, it does not explicitly state when to prefer this over alternatives like label_delete_regions, label_delete_audio_at, or edit_cut, nor does it mention when not to use it.

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