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

With no annotations, the description carries the behavioral disclosure burden. It states what information is contained (source, date, licence, citation) and implies a read-only operation via 'Read this', but it does not explicitly state whether any computation or external interaction occurs. For a simple metadata tool this is acceptable but not fully 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 short sentences with no filler. It front-loads the key content and follows with the actionable purpose. Every word earns its place.

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 parameterless provenance/metadata tool, the description is complete: it names the dataset, the exact fields provided, and the intended use case. No output schema exists, but the listed contents sufficiently set expectations for the response.

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, so the baseline is 4. The description adds meaningful context about what the tool returns, which is more than the empty input schema provides.

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's subject — provenance of the Defectbird dataset — and lists the specific contents (source, computed date, licence, citation). It is distinguishable from sibling dataset tools like dataset_columns or dataset_stats, though it lacks an explicit verb like 'get' or 'show'.

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 this tool is relevant. It does not explicitly discuss alternatives, but none of the sibling tools serve a citation purpose, so the guidance is sufficient.

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