generate_speech

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

With no annotations, the description carries full burden. It discloses key behaviors: multilingual support, API key rotation, model fallback, style instruction prepending, pitch adjustment, and error return. It does not mention destructive nature or auth requirements, but for a TTS tool, this is sufficient.

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 concise and well-structured: a brief intro, then a bullet-style Args list, then a Returns line. Every sentence adds value with no redundancy.

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 moderate complexity (6 params, 1 required) and the presence of an output schema, the description covers all necessary information: functionality, parameter details, and return type. It is fully self-contained.

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 input schema has 0% description coverage, but the description's Args section explains every parameter (text, voice_name, style_instruction, pitch_factor, model, output_path) with defaults and usage details, fully compensating for the schema's lack of descriptions.

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 generates speech audio from text using Gemini TTS. It mentions the key parameters and distinguishes itself from siblings (list_voices, etc.) by focusing on generation, not listing or management.

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 explains the tool's behavior (rotates API keys, falls back between models) and implicitly tells when to use it (for generating speech). It cross-references list_voices for browsing voices, but does not explicitly state 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.