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

With no annotations, the description carries the transparency burden, and the imperative 'Read this' plus a provenance lookup implies a safe, non-mutating operation. It also discloses the returned information, though it does not explicitly state permissions, side effects, or response format.

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 compact sentences front-load the returned fields and then state the reason to call the tool. There is no filler and every clause is useful.

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 zero-parameter, read-only provenance lookup with no output schema, the description supplies the key return fields and the attribution use case. Nothing needed to select or invoke the tool 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 the schema has no per-parameter semantics to explain; the baseline for a zero-parameter tool is 4. The description adds no parameter detail because none exists.

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 resource (Cookievo dataset provenance) and enumerates what it returns: source, computation date, licence, and citation. That is specific enough to distinguish it from the dataset_columns, dataset_stats, dataset_search, and other sibling tools, which expose data rather than attribution metadata.

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?

It gives a clear use case: read this when a figure needs attribution. It does not explicitly list when not to use it or name alternatives, but there are no sibling attribution tools, so the context is sufficient.

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