Image to cinematic video (Video AI)

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

Annotations declare only safety flags (not read-only, non-idempotent, open-world), but the description adds the traits that actually matter: latency ('a few minutes'), the async processing_url contract, and a per-model credit cost (10 aria / 20 symphony). Cost and latency are exactly the behavioral context annotations cannot express.

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?

Front-loaded purpose in the first clause, followed by motion options, then operational notes. The long inline camera-motion list is bulky but arguably necessary because the schema defers to 'listed above'; still, the enumeration dominates the text.

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?

With no output schema, the description correctly supplies the retrieval path (processing_url via luw_get_result) plus cost and latency. It stops short of describing output format, duration, or resolution limits of the generated video, which would help an agent set expectations.

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%, so baseline is 3, but the description adds real value: it enumerates the camera_motion values (which the schema only refers to as 'listed above') and ties the engine enum values to cost tiers. It leaves prompt/seed/enhance_prompt entirely to the schema, which is acceptable.

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?

Specific verb+resource: 'Animate a design image into a short cinematic video', with concrete output examples (fly-throughs, dolly moves, drone shots, reveals). This clearly separates it from image-generation siblings such as luw_generate_image, luw_render, and luw_edit_image.

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?

Provides an important workflow cue — 'Takes a few minutes — expect a processing_url to collect with luw_get_result' — which tells the agent this is async. However, it never states when to choose this over alternatives like luw_run_model or luw_image_to_3d, nor any preconditions on the input image. Usage is implied rather than explicit.

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