Speech segments (VoiceLab)

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

No annotations exist, so the description carries the full burden, and it does well: it discloses the detection method (RMS-based voice-activity, not diarization) and the return contents. It omits auth requirements, failure modes for non-speech audio, and rate/size limits beyond what the schema already states.

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 compact sentences; result description and use cases are front-loaded, with the scope caveat and input options following. 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?

With no output schema, the description correctly specifies returns (timestamps and per-segment RMS), and it covers inputs and the diarization exclusion. Missing only minor operational detail such as error behavior on silent input.

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 100%, so the schema already documents both url and path in full detail, including the mutually-exclusive constraint and inline/upload size thresholds. The description only restates the source options ('public https URL, or a local file path'), adding no syntax beyond the schema. Baseline 3 applies.

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?

States a specific verb+resource ('List voiced speech regions') and enumerates the output (start/end timestamps, per-segment RMS), plus explicitly distinguishes itself from speaker diarization. An agent can tell it apart from analyze_voice and similar siblings without opening a schema.

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?

Names concrete use cases ('auto-trim, chapter generation, speaker-turn detection') and an exclusion ('not speaker diarization'), which is strong context. It doesn't name a sibling alternative to use instead, 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.