label_join_regions

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

No annotations are present, so the description carries the full burden of behavioral disclosure. It usefully notes that 'the labels themselves stay where they are' and positions the tool as the inverse of label_split_regions. However, it does not disclose whether the join is destructive, whether it affects the underlying audio clips permanently, or whether undo is available.

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 front-loaded: the primary action appears in the first sentence. Every subsequent sentence adds necessary operational detail without waste.

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 everything needed to invoke it correctly: what it does, the required selection setup, which tracks it applies to, and the behavior of labels. No critical information is missing.

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 the schema requires no explanation. The description adds meaningful context about selection and scope, going beyond the empty schema. This meets the baseline for a zero-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 pair: 'Join the audio clips across every labeled region back together.' It further distinguishes itself by naming the inverse tool, label_split_regions, so an agent can tell this operation apart from its close sibling.

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 gives clear context for use: it acts on labeled regions within the current selection on selected audio tracks, and instructs the agent to select tracks and a time range first. It names the inverse relationship with label_split_regions but does not explicitly state 'use this instead of X' or list exclusion scenarios.

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