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

Since no annotations are present, the description carries the burden of behavioral disclosure. It names the returned content (source, date, licence, citation) and implies a read-only metadata lookup, but it does not state side-effect-freeness or describe the return shape. That is a gap, though minor for a zero-parameter metadata getter.

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 no filler: the first lists exactly what the tool provides, and the second explains when to use it. Key information (dataset name and attribution purpose) is front-loaded.

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 tool with no output schema, the description covers the essential behavior: what provenance fields are returned and the intended use. It does not specify how the citation is formatted, but an agent can invoke the tool safely without that detail.

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 schema has no parameters, so the description need not explain parameter meaning; 0 parameters earns the baseline 4. The description adds semantics about what the dataset is rather than about call arguments, which is sufficient.

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 identifies a specific resource — provenance metadata for the KnownVehicle dataset (source, computed date, licence, citation) — and its purpose is clear from the title. It lacks an explicit operation verb such as 'retrieve' or 'return', but it is unmistakably distinct 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 concrete use case and tells the agent when to invoke the tool. It does not explicitly exclude alternatives, but none of the siblings return provenance/citation details, so the usage context is clear.

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