kaggle_version_dataset

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

Description explicitly states 'Async' and 'non-destructive (adds a version)', adding value beyond annotations (which only indicate destructiveHint=false). This gives the agent clear expectations about execution and side effects.

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?

Two well-structured sentences with no redundancy. The main action is front-loaded, and key traits (async, non-destructive) are appended concisely. Every word earns its place.

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?

Despite having an output schema and annotations, the description fails to explain parameters, prerequisites (dataset must exist), or return value. For a tool with three parameters, this is incomplete for correct usage.

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?

With 0% schema description coverage, the description adds no meaning to the three parameters (folder, version_notes, delete_old_versions). The agent must infer usage from names alone, which is insufficient for proper invocation.

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 'Push a new version/revision', identifies the resource as an existing dataset, and notes the persistence of engineered features. This clearly distinguishes from sibling tools like kaggle_create_dataset (which creates new datasets) and kaggle_delete_dataset (destructive).

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 states when to use (push a new version) and notes the async, non-destructive behavior. While it does not explicitly exclude alternatives, the context of versioning an existing dataset is clear. A small deduction for lacking explicit when-not guidance.

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