KlingMCP

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

No annotations provided, so description carries full burden. Describes return format ('Table of all models') and content ('capabilities and use cases'), but omits safety characteristics, rate limits, or caching behavior. Functional description present but lacks operational transparency.

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?

Four tight sentences with zero waste: purpose, functionality, usage guidance, and return format. 'Returns:' section efficiently signals output structure. Every sentence earns its place with no redundancy.

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?

Appropriate for a simple discovery tool with zero parameters and existing output schema. Covers purpose, usage context, and return summary. Does not need to elaborate return values further since output schema exists (as indicated by context signals).

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?

Input schema has zero parameters. Per evaluation rules, 0 params = baseline 4. Description appropriately omits parameter discussion since none exist, avoiding unnecessary bloat.

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?

Explicit verb 'List' with specific resource 'Kling models' and context 'for video generation'. Clearly distinguishes from sibling generation tools (kling_generate_video, kling_extend_video) by specifying this is for discovery/model selection rather than production.

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?

Explicitly states 'Use this to understand which model to choose for your video', positioning it correctly in the workflow before generation. Lacks explicit 'when not to use' or contrast with kling_list_actions, but clear positive guidance provided.

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