generate_audio

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

No annotations are provided, so the description carries full burden. It fully discloses async behavior (returns job_id, must poll check_job), expected cost range (50-800 tokens), return format (hosted MP3/WAV URLs), and model-specific behaviors (e.g., TTS auto-detects language, voice clones). No contradictions.

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 relatively long but every sentence adds value. It is front-loaded with the core purpose and async note, then organized into music vs TTS sections. A slight improvement would be using bullet points for the model lists, but it is still effective.

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 complexity (async, multiple models, different usage patterns) and the absence of an output schema, the description covers all necessary aspects: purpose, usage, parameters, behavioral expectations, and limitations. It is complete.

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 the description adds substantial meaning: it explains the default model, lists available music models, details TTS voice presets and cloned voice usage, clarifies that lyrics are optional for music, and sets duration constraints. This goes well beyond the schema's minimal 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 'Generate music OR spoken voice / narration (fal:* models only)', which is a specific verb-resource combination. It distinguishes from sibling tools like generate_image or generate_video by focusing on audio generation and detailing two distinct use cases.

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 when-to-use guidance: music models vs TTS model, with model IDs listed. It advises against using non-fal models (OpenAI/Google) and warns about brand terms causing 422 errors. It also gives prompt construction tips for TTS (read verbatim) and music (genre, mood, etc.).

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