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

With no annotations present, the description carries the full behavioral burden, and it does implicitly signal a safe read by describing static metadata rather than a mutation. However, it says nothing about determinism, permissions, or freshness beyond the computation date, and it does not explicitly confirm the operation is side-effect free.

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 sentences, both earning their place: the first enumerates the payload, the second gives the use case. The most decision-relevant content (what you get) is front-loaded with no filler.

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?

There is no output schema, so the description must describe the return value, and it does so by listing the four fields an agent can expect. Combined with zero parameters and a low-risk read, this is nearly complete; only the exact format of the citation/licence response is left unspecified.

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 schema declares zero parameters and zero required parameters, so there is nothing for the description to explain. The baseline for a parameterless tool applies, with no gaps 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 names the exact fields returned (source, computation date, licence, citation) for a specific resource, the Calibvo dataset, so an agent knows precisely what this tool surfaces. It is readily distinguishable from siblings like dataset_stats or dataset_columns, though it never states an explicit retrieval verb and relies on the reader to infer that this is a read.

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 trigger condition for when to call the tool, which is more than most metadata tools offer. It stops short of naming an alternative or an exclusion, but for a zero-argument lookup the routing decision is unambiguous.

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