Run Dataset Discovery Skill

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

With no annotations provided, the description alone must reveal behavior. It does mention 'searches, ranks, and explains' and 'structured workflow', which gives a rough sense of the process, but it does not disclose important behavioral traits such as whether the skill orchestrates other tools, whether it writes files, whether it requires user confirmation, or what the final output format looks like.

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 short and front-loaded with the main action, which is acceptable. However, it is a single clause that gives minimal information, and the opening 'Run the Dataset Discovery skill' largely echoes the tool name rather than adding new, substantive detail.

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?

The tool has a required task parameter, four optional filter-like parameters, no output schema, no annotations, and many siblings; this description is far too thin to serve as adequate context. It does not explain what task should contain, what the workflow's output is, how the ranking works, or how it relates to other dataset discovery/skill tools. An agent cannot confidently invoke it correctly based on this definition.

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% and the description does not compensate. It does not explain parameters such as task, limit, modality, license, object_class, access_requirement, or annotation, nor how they affect the search/ranking workflow. The agent would have to guess the meaning and purpose of all seven parameters from their names alone.

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 clearly states the action ('Run the Dataset Discovery skill as a structured workflow') and the outcome ('searches, ranks, and explains dataset candidates'). It communicates the resource and core behavior, but it does not explicitly contrast itself with siblings like find_datasets or search_resources, so it does not fully maximize distinguishing power.

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 sibling tools such as find_datasets, get_skill, or search_resources. The phrase 'skill as a structured workflow' only implies the user should choose it when they want a full workflow rather than a direct resource lookup, but no explicit alternatives or conditions are given.

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