kling_list_models

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

Given no annotations, the description sufficiently indicates a read-only operation that returns a table. The output schema covers return details, so the description adds context about use cases without needing extensive behavioral disclosure.

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 somewhat repetitive, e.g., 'List all available...' and 'Shows all available...' convey similar information. The structure is adequate but could be more concise.

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 a simple listing tool with no parameters and an output schema, the description provides sufficient context about its purpose and use. It is lacking explicit guidance on ordering (e.g., call this before generation) but overall complete enough for an agent.

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?

The input schema has no parameters and is fully covered (100% coverage). The description does not need to add parameter semantics, so it meets the baseline without extra value.

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 explicitly states it lists all available Kling models for video generation, covering capabilities and use cases. This clearly distinguishes it from sibling tools which focus on generation or extension tasks.

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 advises using this tool to choose a model for video generation, providing clear guidance on when to use it. However, it does not explicitly mention when not to use it or alternatives.

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