generate_video

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the need to discover models via list_models, it lacks critical details such as whether this is a read-only or destructive operation, potential rate limits, authentication requirements, or what the output format looks like (e.g., video URL, file). This leaves significant gaps for an AI agent to understand the tool's behavior.

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 appropriately sized and front-loaded, with two concise sentences that directly state the tool's purpose and usage guidance. Every sentence earns its place without redundancy, making it efficient and easy to parse.

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 complexity of a video generation tool with 7 parameters, no annotations, and no output schema, the description is incomplete. It fails to address key behavioral aspects (e.g., mutation effects, output format) and does not compensate for the lack of structured data, leaving the AI agent with insufficient context for safe and effective use.

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 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by hinting at model discovery but does not provide additional semantic context for parameters like prompt or image_url. Baseline score of 3 is appropriate as the schema does the heavy lifting.

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's purpose with specific verbs ('generate videos') and resources ('from text prompts' and 'from images'), distinguishing it from sibling tools like generate_image or generate_music by specifying video generation. It explicitly mentions both text-to-video and image-to-video capabilities.

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 clear context on when to use this tool (for video generation from text or images) and references an alternative tool (list_models) for discovering available models. However, it does not explicitly state when NOT to use it or compare it to similar siblings like generate_video_from_image, which might cause ambiguity.

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