voice_speak

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

With no annotations provided, the description clearly explains the fallback mechanism and what the tool does. It reveals behavioral traits (play aloud, fallback) but doesn't cover potential failure modes or side effects.

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 two sentences long and front-loads the core purpose. Every sentence adds value without unnecessary detail.

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?

Given the tool's simplicity, the description covers the main functionality. However, it does not mention what the tool returns (though an output schema exists). It is nearly complete for a straightforward 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?

The description adds no additional meaning beyond the parameter names and types. It does not explain acceptable voice values, default behavior when voice is null, or format requirements for text. Schema coverage is 0%, so the description should compensate but fails.

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 action ('Convert text to speech') and the output ('play it aloud'). The tool name 'voice_speak' is well-chosen and there are no other voice-related siblings, making it distinct.

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 provides helpful context about fallback behavior between neural TTS and system engines, but does not explicitly state when to use this tool versus alternatives (none exist) or 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.