search_kernels

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

No annotations are provided, so the description carries the burden of behavioral disclosure. 'Search' implies a read-only lookup and 'public' clarifies scope, but the description does not mention sorting, filtering, pagination, or what the response contains. This is minimal but not misleading.

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 a single, front-loaded sentence with no filler or repetition. It is appropriately brief for stating the core purpose, though it is so terse that it omits important guidance that other dimensions require.

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 eight parameters, zero schema descriptions, no annotations, and no output schema, the description is far too thin to support correct invocation. It provides only the basic purpose and leaves filtering, sorting, result shape, and usage trade-offs completely unspecified.

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%, so the description must compensate for the eight undocumented parameters, but it mentions none of them. The agent gets no help understanding query, user, dataset, limit, sort_by, language, competition, or kernel_type beyond their raw names and types.

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 states a specific verb ('Search') and a clear resource ('public Kaggle notebooks/kernels'). The word 'public' and the resource type distinguish it from siblings like search_datasets and list_my_kernels, so an agent can tell this tool apart without opening the schema.

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 context by specifying 'public' kernels, but it does not explicitly say when to prefer this tool over alternatives such as search_datasets or list_my_kernels. There is no when-not-to-use guidance or named alternative.

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