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

With no annotations provided, the description carries the full burden of disclosure. It names the exact contents returned (source, date computed, licence, citation) and frames the operation as a read, which implies no side effects. It does not elaborate on output format, but that is a minor omission for a simple provenance lookup.

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 two short sentences with no filler. The content summary is front-loaded and the intended use case is stated immediately, making it easy for an agent to parse.

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 provenance metadata tool with no output schema, the description sufficiently covers what it returns and why an agent would call it. The title reinforces the purpose. Nothing critical is missing for correct invocation.

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?

There are no parameters and the schema is empty, so parameter documentation is not needed. The description instead explains what information the tool returns, which is the relevant semantic content for this tool.

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 identifies the tool as the provenance/citation source for the Amortlane dataset, listing what it returns: source, computation date, licence, and citation. This distinguishes it from sibling tools like dataset_columns, dataset_stats, or dataset_search, which operate on data content rather than 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?

The description explicitly states when to use it: 'Read this to attribute a figure correctly.' It gives a clear use case, though it does not explicitly mention alternatives or when not to use this tool; however, the sibling tools are sufficiently distinct.

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