speak_text

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

No annotations provided, but description fully covers behavior: non-blocking (returns 200 immediately), interrupt default (true) with queue option, platform-specific voice/rate, and Linux requirement. Also mentions error handling (failure returns structured hint).

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?

Description is a single paragraph but efficiently conveys purpose, usage, behavior, and parameter details. Could be slightly more structured (e.g., bullet points) but remains concise and front-loaded.

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?

Tool has 4 params, no output schema. Description covers all essential aspects: operation, use cases, parameter defaults, platform considerations, dependency, and failure mode. No gaps identified.

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%, but description adds value: max length on text, interrupt default true, rate as words-per-minute on macOS, and advice to use list_tts_voices for voice. Only minor improvement would be clarifying rate default format for other platforms.

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 tool speaks text aloud via system TTS, and distinguishes from sibling tools like stop_speaking and list_tts_voices by providing use cases (when user can't see screen) and alternatives for voice selection.

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?

Description explicitly states when to use ('when user can't see the screen') and provides examples (confirmations, read-backs). It recommends calling list_tts_voices first for specific voices and notes Linux dependency. Lacks explicit when-not-to-use but sufficiently guides usage.

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