Search ML Datasets

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

Annotations already provide readOnlyHint and idempotentHint. The description adds useful behavioral details: returns specific fields (dataset ID, downloads, likes, tags), covers multiple domains (NLP, vision, audio, tabular), and sorting by downloads. This goes beyond annotations.

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 extremely concise with three sentences, front-loading the primary purpose. Every sentence adds value, and there is no repetition or fluff.

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 the tool's simplicity, schema coverage, and annotations, the description covers core functionality, return fields, and scope. It lacks details on error handling or empty results, but for a search tool, it is sufficiently complete. A score of 4 reflects minor 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 coverage is 100% with descriptions for both parameters. The description does not add new meaning beyond what's in the schema (e.g., examples or format details). Baseline 3 is appropriate.

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 it searches datasets on HuggingFace Hub by name or keyword, specifying the resource ('datasets') and verb ('Search'). It differentiates from sibling tools like 'hf.hub.model_details' and 'hf.hub.models', which focus on models, not datasets.

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 implies when to use (search datasets) but does not provide explicit guidance on when not to use or alternatives. No mention of limitations or comparison with other tools. A score of 3 reflects adequate but minimal usage guidance.

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