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

No annotations are provided, so the description carries the burden. It clearly lists the four pieces of information returned and implies a read-only operation ('Read this'). It doesn't describe return format or error cases, but for a simple metadata tool this is adequate.

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 concise sentences, front-loaded with the core content (source, date, licence, citation). Zero wasted words and the use case is clearly stated.

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 returning static metadata, the description covers the essential content. It doesn't describe the exact return structure, but given the simplicity, an agent can reasonably infer the output from the listed items.

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 no parameter-specific detail, but none is needed.

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 states the tool provides provenance information: source, computation date, licence, and citation for the Weekrota dataset. This is a specific verb+resource (retrieve provenance for dataset) and clearly distinct from sibling tools like dataset_columns or dataset_stats.

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 says 'Read this to attribute a figure correctly,' giving a clear when-to-use scenario. It doesn't mention alternatives or exclusions, but the purpose is so specific that ambiguity is minimal.

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