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

With no annotations, the description carries the full burden, and it does convey that this is a passive metadata read with no arguments. However it says nothing explicit about side effects, idempotency or whether the tool ever mutates anything — it is trivially safe by construction, but the safety profile is inferred, not stated.

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, zero filler, with the returned fields front-loaded before the usage clause. Every sentence 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?

No output schema exists, and the description compensates by naming the four pieces of information returned, which is what an agent needs to decide whether to call it for attribution. It could go slightly further by noting the format of the citation or whether the data is stable, but it is sufficient for a zero-argument tool.

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 takes zero parameters, so per the rubric the baseline is 4. There is nothing further for the description to document 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?

Names the exact resource (the Excurvo dataset's provenance) and enumerates what it returns: source, computation date, licence, citation. Against siblings like dataset_columns, dataset_stats and dataset_search, which all expose data rather than metadata, an agent can tell this one apart immediately.

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 when-to-use condition, which is unusual and helpful for a lookup tool. It stops short of naming alternatives or stating exclusions, so it is a clear context statement rather than full routing guidance.

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