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

Beyond the annotations (which only indicate non-read-only, open-world, non-idempotent, non-destructive), the description discloses critical behavioral traits: it submits and polls with an up-to-8-minute budget, default fallback to fal.ai, cost surcharges for audio on Kling models, and the fact that resolution/duration drive cost more than model choice. It also mentions auto-tagging for NSFW. No contradiction with annotations.

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 long, but every section earns its place: the opening paragraph is a clear summary, and the model selection guide is well-structured with terse bullet lines. It is front-loaded with the core behavior. It could be slightly tightened by moving the cost guide elsewhere, but the structure is logical and scannable, making it more genuinely useful than verbose.

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 tool's complexity (8 params, many model choices, no output schema), the description is remarkably complete. It explains the return value (final video URL), poll timing, model selection criteria, cost pitfalls, and when to ask the user. The absence of an output schema is compensated by the explicit mention of the returned URL and the polling behavior.

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 baseline is 3. The description adds meaningful context beyond the schema: it notes imageUrl is required for I2V models, endImageUrl is for first+last-frame models, duration constraints per model (e.g., Grok accepts 6-15s), and cost implications of resolution/duration choices. It does not systematically walk through every parameter but enriches the most decision-critical ones.

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: 'Generates a short-form video from a text prompt (T2V) or a text prompt + starting image (I2V).' It clearly distinguishes itself from sibling tools like aetherwave_generate_image and aetherwave_generate_music by focusing exclusively on video generation, and further clarifies T2V vs I2V modes.

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 an extensive, explicit model selection guide with concrete conditions ('highest quality', 'fast premium', 'cinematic camera moves', etc.) and names alternative models like veo3.1-quality, seedance-pro-t2v, and wan-2.7-t2v. It also states when to ask the user (e.g., cost >100 credits) and when to use list_video_models, giving clear decision boundaries.

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