normahub_image_to_video

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

With no annotations, the description carries the full burden; it usefully discloses the asynchronous polling behavior ('Automatically polls until video render completes'), which is real added value. It says nothing about expected latency, failure/timeout handling, or cost/credits, which matter for a generation call.

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?

Three short sentences, front-loaded with the core action, with no padding. The middle sentence ('Accepts image URL and animation prompt') mildly restates the schema but 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?

For an async generation tool with no output schema and no annotations, the description covers input intent and polling but leaves the return value (presumably a video URL), latency expectations, and failure modes unspecified.

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 three parameters, giving a baseline of 3. The description adds no constraints, sizing limits, or guidance on choosing between models, and it omits the grok-imagine-video-1.5 option listed in the schema.

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?

States a specific verb+resource ('Animate an input image into a video') and names the underlying models and API, so intent is unambiguous. It never explicitly contrasts with the sibling normahub_generate_video, leaving the agent to infer that this variant is the image-conditioned one.

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 requirement for an input image implicitly routes the agent away from text-to-video siblings, but there is no explicit when-to-use/when-not guidance and no mention of alternatives such as normahub_generate_video or normahub_prompt_model.

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