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

With no annotations, the description carries the behavioral burden. It discloses the contents of the provenance information, but does not describe the return format, whether it is a static metadata lookup, or any failure cases. This is adequate for a simple metadata tool but not richly transparent.

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 sentences with no filler or redundancy. It front-loads the key content fields and then gives a direct usage instruction.

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 simple zero-parameter provenance tool, the description enumerates the expected fields and the purpose. While there is no output schema, the listed fields themselves give an agent enough understanding of what will be returned.

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 has zero parameters and an empty input schema, so parameter documentation is unnecessary. The baseline of 4 applies because there is nothing for the description to compensate for.

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 exposing provenance metadata—source, computation date, licence, and citation—for the Lobbyvo dataset. It is distinguishable from siblings like dataset_row or dataset_stats by its focus on attribution rather than data access or analysis, though it lacks an explicit action verb.

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 instruction 'Read this to attribute a figure correctly' gives a concrete use case and tells an agent when this tool is relevant. It does not explicitly name sibling alternatives or state when not to use it, but the context is clear enough for a zero-parameter provenance lookup.

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