kaggle_list_kernels

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral details: output is a 'compact table' and supports pagination. No contradiction with annotations.

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 extremely concise (three short phrases) and front-loaded with the primary action. Every word adds value 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?

Given the tool has 7 parameters with 0% schema description coverage, the description should explain key parameters like 'search', 'page', 'sort_by'. It only mentions pagination generally. The output schema exists but is not detailed; the description does not clarify return structure beyond 'compact table'.

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 0% with 7 parameters, but the description adds no information about any parameter (e.g., search, page, language). The AI must infer usage solely from parameter names and types, which is insufficient.

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 clearly states the verb 'List/search' and resource 'notebooks (kernels)', specifies output format 'compact table', and notes pagination. It is distinct from sibling tools like 'kaggle_pull_kernel' which retrieve specific kernels.

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 implies usage for listing/searching kernels but does not explicitly state when to use this over alternatives. No when-not-to-use guidance is provided, though sibling names give some context.

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