generate_gemini_tts

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

No annotations exist, so the description carries full burden. It thoroughly discloses the tool's behavior: it uses Google Gemini, supports style direction and tone tags, has credit cost (~4.2 credits/min), model trade-offs (flash quality degrades on long takes, pro stable), async mode via wait=false, and default download location. This is comprehensive behavioral transparency.

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 approximately 150 words, tightly packed with key information: novelty ('NEW'), backend, capabilities, pricing, modes, model comparison, and output location. It is front-loaded with the most critical identifier ('Google Gemini native TTS'). No redundant sentences; every sentence earns its place.

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 12 parameters and no output schema, the description covers core functionality, limitations, async workflow, and pricing. It explains the two modes and model selection well. However, it does not describe the return format (e.g., whether it returns a file path or URL after download) or error cases, leaving a minor gap in completeness.

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 description coverage is 92% (11/12 params have inline descriptions). The description adds high-level context about modes and credits but does not significantly elaborate on individual parameter meanings beyond what the schema already provides. For example, 'scene' and 'sample_context' are better explained in the schema than in the description. Baseline 3 is appropriate as the description adds moderate value but does not deeply augment parameter semantics.

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 it is a Google Gemini native TTS tool with style-directed speech, 30 named voices, up to 2 speakers, and inline tone tags. It distinguishes itself from siblings (generate_tts, generate_dialogue) by highlighting the Gemini backend, pricing, and dual modes (simple/dialogue). The verb 'generate' and resource 'gemini_tts' are specific 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?

The description provides explicit guidance on when to use each mode (Simple vs Dialogue) and which model to choose (flash for expressive <60s, pro for stable long-form). It also mentions pricing and compares to ElevenLabs. However, it does not explicitly name alternative tools (e.g., generate_tts) for cases where Gemini is not desired, slightly limiting differentiation from siblings.

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