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

No annotations are supplied, so the description carries the full burden. It implies a safe read by describing static metadata, but never states the tool is read-only, whether the provenance reflects the live or a cached snapshot, or whether any prerequisite (e.g. dataset access) applies. Adequate but not rich.

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 with no filler: the returned content is front-loaded in the first sentence and the usage cue follows. Every clause 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?

Because there is no output schema, the description must describe the return surface, and it does list the four things the agent will get. It leaves minor ambiguity about the exact format/field names returned, but the essentials for correct invocation are present.

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 by the rubric the baseline is 4. Nothing in the description contradicts or confuses the empty input schema.

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 enumerates exactly what the tool returns (source, computation date, licence, citation) for a named dataset, which is far more specific than a restatement of the name. It implicitly differentiates from siblings like dataset_stats or dataset_columns by being the only metadata/attribution tool, though it never explicitly contrasts with them.

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 triggering condition for calling the tool. It stops short of naming alternatives or exclusions, so the agent still has to infer there is no overlap with the other dataset_* tools.

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