llm_audio

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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It mentions routing to external services but does not disclose API dependencies, potential costs, latency, or side effects. The agent lacks critical context about external service requirements.

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 front-loaded with the core purpose in one sentence, followed by a compact argument list. It is efficient but could be slightly more concise by removing the 'Args' docstring format in favor of inline text.

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?

The description explains parameter options but lacks important context such as return value type (audio data) and any output schema details. Considering the output schema exists, the description is adequate but not fully complete in behavior and usage context.

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 0%, but the description adds meaningful details for all three parameters: it explains the text input, lists specific model options (e.g., 'openai/tts-1-hd'), and enumerates voice choices for both providers. This compensates well for the schema gap.

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 and explicitly mentions routing to ElevenLabs or OpenAI TTS. It distinguishes itself from sibling tools like llm_image or llm_video which handle other modalities.

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 implies usage for text-to-speech conversion but does not explicitly state when to use this tool versus alternatives (e.g., other audio generation tools), nor does it provide any when-not-to-use guidance.

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