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

Annotations declare the write/open-world profile, and the description adds genuinely new behavioral context: approximate credit cost (std ~20, pro ~35) and the fact that this returns an asynchronous job 'like kling_text_to_video', implying polling via kling_get_task. It does not state job duration or failure behavior, so it stops short of 5.

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 terse fragments that front-load the core action, then the prerequisite tool, then cost and async semantics. No filler and nothing buried.

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 job tool with a fully documented schema and no output schema, the description covers the essential unknowns: which tool supplies effect names, what it costs, and that it returns a job. The missing piece is the relationship to kling_image_to_video and how results are retrieved.

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 the baseline is 3, but the description adds decision-relevant meaning for the mode parameter by pricing std versus pro, which helps the agent choose among the enum values. It does not explain the 4k tier or the prompt limiting conditions.

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 and resource ('Turn an image into a video with one of Kling's effects') and points at the endpoint. However, it never distinguishes itself from the closest sibling, kling_image_to_video, which also turns an image into video, so an agent must infer which path to take.

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?

It gives the credit cost for std/pro and directs the agent to kling_list_effects for valid effect names, which is a real usage requirement. But it offers no when-to-use guidance relative to kling_image_to_video or when-not conditions, leaving the main selection decision implicit.

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