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

With no annotations provided, the description carries the behavioral burden. It frames the tool as a read operation ('Read this') and enumerates the returned content, which is useful. However, it does not explicitly disclose side effects, response structure, or access constraints, though for a zero-parameter metadata tool these risks are limited.

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, no filler. The most important information—source, date, licence, citation—is front-loaded, and the directive 'Read this to attribute a figure correctly' earns its place by stating the intended use.

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 metadata tool with no output schema, the description covers the essentials: what data it concerns, what fields it returns, and why to call it. The only minor gap is not specifying exact value formats, but that is not necessary for correct invocation here.

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 input schema is empty, so the parameter-semantics baseline is 4. The description appropriately adds meaning by naming the dataset and the output fields, even though there are no parameters to document.

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 states a specific resource (the Threewayly dataset) and the exact fields returned: source, computed date, licence, and citation. The title 'Where this data comes from, and how to cite it' plus the attribution use case clearly distinguishes it from the sibling data-manipulation tools.

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 clear and practical when-to-use condition. It does not explicitly mention alternatives or exclusions, but the sibling tools are different enough in purpose that the intended use is evident.

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