voice_note

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

No annotations are present, so the description carries the full burden. It discloses that notes persist across rebuilds and matter more than statistics, which is useful behavioral context. However, it does not mention return value or whether notes overwrite or append, which is a minor gap but acceptable for such a simple tool.

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 three sentences, each with a distinct role: purpose, usage triggers, and persistence context. It is front-loaded with the core function and contains no filler or repetition.

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 single-parameter tool with no output schema, the description covers the purpose, when to use it, content guidelines, and persistence behavior. It is fully self-contained and leaves no critical questions unanswered about how or why to invoke the tool.

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?

Despite 100% schema coverage, the description significantly enhances the parameter's meaning by providing concrete examples of what to record (banned phrases, audience, corrections, running jokes). This goes far beyond the schema's generic 'rule or observation' and helps the agent craft effective notes.

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 explains that the tool records qualitative notes about the user's voice, with specific examples of what to record. This distinguishes it from sibling tools like voice_get, voice_score, and voice_learn, which handle measurement, analysis, or learning. The verb 'Record' and resource 'something about the user's voice' are concrete and unambiguous.

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 explicitly states when to use the tool: 'Use this whenever the user says...' with concrete trigger phrases like 'I wouldn't say that' or 'too formal'. This provides clear decision guidance, and the distinction from statistical measurement implies that this is for subjective, non-measurable feedback.

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