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

With no annotations provided, the description carries the disclosure burden. It names the exact information returned (source, computation date, licence, citation) and frames the operation as a read-only lookup. This is adequate for a simple metadata tool, though it does not explicitly state that no data is modified.

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 core content is front-loaded in the first sentence, and the second sentence adds practical guidance about when to use it. 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 zero-parameter, no-output-schema tool, the description lists the important returned fields and the intended use case. It could be slightly more explicit about the output format, but the stated fields are sufficient for an agent to interpret the result and cite the dataset correctly.

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 the schema confirms this. According to the rubric, a no-parameter tool starts at baseline 4, and no parameter description is needed. Nothing is missing here.

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 providing provenance information for the Pickpathly dataset: source, computed date, licence, and citation. It is easily distinguished from sibling data-access tools like dataset_row or dataset_stats, though it lacks an explicit verb such as 'returns' or 'retrieves'.

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 phrase 'Read this to attribute a figure correctly' gives a clear usage context: use this tool when citation or attribution is needed. It does not name alternatives or explicitly say when not to use it, but the context is specific enough for an agent to route correctly.

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