generate_data_card

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the output (structured data card) and extracted details, but does not mention potential side effects, authentication needs, or idempotency. The transparency is adequate but not detailed.

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 concise and front-loaded with the main purpose, followed by a bullet list of extracted attributes and parameter/return sections. The structure is clear, though the 'Parameters' and 'Returns' headers are slightly redundant given the schema. Overall efficient.

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 (one integer parameter, no nested objects, no output schema), the description is complete enough. It explains the purpose, the input, and the output format. Slight lack of detail on the return dict structure is acceptable, as it's an AI-friendly summary.

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 0%, requiring the description to compensate. The description provides a clear and sufficient description for the single parameter record_id ('The Zenodo record ID.'), fully covering its semantics. This exceeds the baseline expectation.

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 produces a concise, structured AI-friendly summary (data card) of a dataset, specifying verb (Produce), resource (data card of dataset), and extracted attributes (modality, file formats, etc.). This distinguishes it from sibling tools like get_record (full record) or evaluate_reusability (deeper analysis), earning a top score.

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 usage for obtaining a quick dataset summary, but lacks explicit guidance on when to use this tool versus alternatives such as evaluate_reusability or get_record. No exclusions or prerequisites are mentioned, so the guidance is only implied.

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