llm_video

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

No annotations are provided, so the description must bear full responsibility. It mentions routing to multiple models but lacks any details on behavior such as asynchronous processing, error handling, rate limits, or what happens when a specified model is unavailable. The description does not disclose potential costs or permissions needed.

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 concise: one sentence plus a labeled argument list. Every part serves a purpose. It could be slightly more structured (e.g., separating the general behavior from arguments), but it is not wasteful.

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 that an output schema exists, the description does not need to detail return values. However, it omits important contextual information such as whether the tool returns immediately or asynchronously, error scenarios, or cost implications. The tool's complexity (video generation, multiple models) suggests that more completeness would be beneficial.

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?

The input schema has 0% description coverage, so the description adds value by explaining each parameter: prompt ('Description of the video to generate'), model (with examples like 'gemini/veo-2'), and duration ('Video duration in seconds (default: 5)'). However, the descriptions are minimal and do not include constraints (e.g., duration range, model availability) beyond the defaults.

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 a video — routes to Gemini Veo, Runway, Kling, or other video models.' This specifies the verb (generate) and resource (video), and distinguishes it from sibling tools like llm_audio and llm_image by mentioning routing to multiple video models.

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 implied through the tool name and description (use for video generation), but there is no explicit statement of when to use this versus alternatives, nor any when-not-to-use scenarios or prerequisites. For example, it does not mention that this tool may be slower or require more credits.

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