generate_speech

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

No annotations exist, so the description carries the full burden, and it does disclose two real behaviors: audio is returned inline, and a private, expiring download link is produced only if a Blob store is connected. It omits cost, text-length limits, latency, and whether the link's expiry/size is configurable, leaving meaningful gaps for a no-annotation 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?

Three sentences, front-loaded with the core action, then return behavior, then model/voice discovery. No filler, no restatement of the tool name, and the list_models pointer is placed exactly where model selection becomes relevant.

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 no output schema, the description correctly compensates by describing the return (inline audio plus conditional expiring Blob link). Combined with a fully-described 7-param schema, an agent has enough to invoke correctly; only cost/length/rate expectations are unaddressed.

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 already 100%, so the baseline is 3, but the description adds genuine selection value: it enumerates concrete model ids ("openai/tts-1-hd", "google/gemini-3.8-flash-tts", etc.) and voice examples ("alloy", "nova", "onyx") that the schema only describes generically. It stops short of explaining interactions like model-dependent support for "instructions" or "language".

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?

Opens with a specific verb+resource ('Turn text into spoken audio') and immediately scopes the domain with concrete use cases (narration, voice-overs, audiobook chapters, podcast intros). It is unmistakably distinct from siblings generate_text, generate_image, transcribe, and start_video without opening any schema.

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 routes the agent to list_models with type "speech" to discover models and prices, and warns that voices are model-specific with per-model examples. However it gives no explicit exclusions or when-not-to-use guidance relative to siblings like transcribe (speech-to-text) or generate_text.

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