kaggle_dataset_check

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

With no annotations, the description carries the full burden. It discloses the core behavior (an existence check) and implies a read-only operation, but it does not mention network dependence, authentication, or failure behavior. It is not misleading, but it is thin.

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?

A single concise sentence with no filler. Every word earns its place, and the key scoping detail (owner/slug) is front-loaded.

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 a single required parameter and the presence of an output schema, the description is adequate for basic invocation. The main gaps are explicit usage guidance and parameter format examples, but the simple nature of the check makes this acceptable.

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 schema only lists 'dataset_slug' with no description. The description clarifies that the parameter is the 'owner/slug' combined identifier, which is valuable information not apparent from the schema. However, it stops short of providing a concrete format example.

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 ('check') and a specific resource ('Kaggle dataset owner/slug existence'), which clearly distinguishes it from sibling tools that push, status, or log. The purpose is immediately understandable.

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 does not explicitly state when to use this tool or mention alternatives, though the sibling kaggle_dataset_push implies this check would be performed before pushing a dataset. The usage context is implied but not stated.

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