kaggle_kernel_status

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Poll' suggests a read-only status check, but the description does not say whether it blocks until completion, what statuses are returned, how often it should be polled, or how it behaves on failure.

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 short sentence with no filler. The verb and object are front-loaded, making it immediately scannable and efficient.

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?

Although this is a simple one-parameter tool and an output schema exists, the description lacks essential usage context: when to poll, how the status result should be interpreted, and how this complements or differs from sibling kernel tools. The minimal information leaves too much for the agent to infer.

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 description adds no meaning to the sole parameter, kernel_slug, beyond what the schema field name already implies. With 0% schema description coverage, the description was expected to clarify what a kernel slug is or where to find it, but it does not.

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 uses a specific verb, 'Poll', and a clear resource, 'Kaggle kernel execution status', which makes the tool's core purpose obvious. It is distinguishable from siblings like kaggle_kernel_push and kaggle_kernel_logs by the word 'status', though it does not explicitly name or contrast 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?

There is no guidance on when to use this tool versus alternatives such as kaggle_kernel_logs or kaggle_kernel_output_to_drive. The description implies a polling workflow but does not state prerequisites, timing, or conditions for choosing this tool.

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