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

No annotations are supplied, so the description carries the full behavioral burden. With zero parameters and no output schema, it usefully enumerates the returned fields (source, date, licence, citation) and implies a read-only metadata lookup, though it never states read-only status, caching, or freshness guarantees explicitly.

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 short sentences with the returned content front-loaded and the use case trailing; every clause earns its place and there is no filler or repetition of the title.

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?

For a parameterless, annotation-free tool with no output schema, the description does the necessary work by listing the exact fields an agent will receive and telling it why to call the tool. Nothing required to invoke it correctly is missing.

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 tool takes zero parameters, so there is nothing for the description to disambiguate; baseline is 4. Schema coverage is reported as 100%, but with an empty properties object it is simply a parameterless call with no syntax to explain.

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 names the exact content the tool returns (source, computation date, licence, citation) for a named dataset, which cleanly separates it from the data-oriented siblings (dataset_columns, dataset_stats, dataset_search, dataset_row). An agent can tell what this tool produces without opening any schema.

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?

"Read this to attribute a figure correctly" gives a concrete triggering situation, which is exactly when an agent would need provenance rather than raw values. It does not name an explicit alternative or a when-not condition, but no sibling overlaps this purpose, so the missing exclusion is low-cost.

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