Generate Sound Effect

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

The description discloses the async submission behavior and the need to poll for results, which annotations don't convey. It also clarifies the surprising constraint that video_url is required even for text-driven models. It doesn't mention rate limits or side effects, but given the annotations (readOnlyHint=false, destructiveHint=false), the behavioral disclosure 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 well-organized: a one-sentence purpose, followed by key distinctions, async workflow, and a compact Args list. It is front-loaded with the most critical info and every sentence contributes useful guidance without padding.

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?

For a 4-parameter async generation tool with no output schema, the description covers the essential operational context: model selection guidance, required video_url constraint, async polling flow, and duration limits. It is complete enough for an agent to invoke and follow up correctly.

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%, and the schema itself includes detailed descriptions for all parameters. The description's Args section largely mirrors the schema, adding only the emphasis that video_url is required for all models. Since the schema already carries the semantic load, the description adds marginal value, warranting the baseline score.

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 opens with a specific verb+resource: 'Generate foley / ambient sound effects for a given video.' This clearly states what the tool does and distinguishes it from sibling tools like image generation, TTS, or music generation. It further clarifies model subtypes (video-driven vs text-driven), making the purpose 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?

Usage guidance is explicit: it explains that video_url is required for every model, describes when to use text-driven vs video-driven models, and instructs the caller to poll xbrush_get_request with the returned request_id. This gives clear when-to-use and next-step context, going beyond simple purpose.

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