kaggle_kernel_push

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. It states a push action but does not say whether this creates a new kernel, updates an existing one, requires authentication, or has side effects like overwriting content.

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 two short sentences with no filler, and the key purpose is front-loaded. The added dataset_slugs format note is directly useful, though the overall brevity leaves some gaps.

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?

For a 5-parameter tool with no annotations, the description is too minimal to be complete. It does not explain most parameters, prerequisites, effects, or when to choose this tool over its siblings; the output schema mitigates return-value ambiguity but not the other gaps.

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, but it only clarifies dataset_slugs as a comma-separated owner/slug list. The other four parameters—kernel_slug, script_content, title, and enable_gpu—receive no semantic explanation beyond their schema titles.

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?

"Push a Kaggle script kernel" names a concrete action and resource, making it immediately clear what the tool does. The kernel resource also distinguishes it from sibling tools like kaggle_dataset_push and kaggle_kernel_status.

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 gives no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. The sibling tool names imply a kernel-focused workflow, but that is left to inference rather than stated.

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