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

Annotations already declare readOnlyHint=true and openWorldHint=false. The description adds real value beyond that: it describes the return payload (slug, required input type, kavel.ai page) and discloses the important behavior that video tools cannot be executed here but open externally.

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 tight sentences, front-loaded with what the tool does, followed by return shape and the run/open distinction. No filler or repetition.

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 compensates by spelling out the returned fields (slug, input requirement, kavel.ai page) and the image-vs-video execution path. An agent has everything needed to call it and act on results.

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 both parameters are already documented, and the hub enum is self-explanatory. The description only reinforces their intent ('photo or just text', search-oriented query) without adding syntax or format detail, so the baseline 3 is appropriate.

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 ('Search') and resource ('Kavel's AI photo and video effects') and immediately gives concrete examples (hairstyle changer, figurine, yearbook photo). It also distinguishes itself from the sibling generate_kavel_image by clarifying that searching finds tools while generating runs them.

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 routes the agent: image tools can be run via generate_kavel_image, video tools open on kavel.ai. That is a clear use-context, though it doesn't compare against check_kavel_generation or get_kavel_balance, and gives no explicit when-not-to-use.

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