generate_video

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

Annotations already show non-destructive and open-world. Description adds key behavioral context: asynchronicity, immediate video_id return, polling requirement, and cost per second. 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?

Compact single paragraph of 4 sentences, front-loaded with async nature, then polling, optional image, and model cost. No wasted words.

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?

Covers async job, polling, model selection, optional image, and output format (video_id then MP4). Lacks error/rate limit details but sufficient for typical 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 covers 100% of parameters with descriptions. Description adds value with cost implications for model choice, default seconds, and use of image_url for image-to-video. Slightly above baseline.

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?

Clear verb 'Start an ASYNCHRONOUS video generation job' with specific resource 'Google's Veo models'. Distinguishes from siblings like get_video (polling) and generate_image (static).

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?

Explicitly tells when to use (async video generation), how to get results (poll with get_video every ~10s), and model selection advice (veo-3.1 for quality, veo-3.1-lite for drafts). Context for sibling tools is clear.

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