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

With no annotations, the description carries the full burden. It discloses what metadata is returned (source, date computed, licence, citation), which is the meaningful behavior for a zero-parameter metadata lookup, but says nothing about auth requirements, caching, or whether the values are static. For an inherently side-effect-free read this is adequate, 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 sentences, no filler. The returned fields are front-loaded and the imperative 'Read this...' follows immediately, so the agent gets content and purpose in one pass.

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, so the description must describe the return values, and it does so by enumerating all four fields. Combined with the absence of parameters, that is nearly everything an agent needs; only the response shape is left implicit.

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 there is nothing for the description to disambiguate. Baseline of 4 applies; no parameter semantics are needed or missing.

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 resource (the Issafu dataset's provenance) and enumerates the concrete fields it exposes: source, computation date, licence, and citation. That is specific enough to separate it from siblings like dataset_columns or dataset_stats, though it is a noun list with no verb.

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 an explicit use case that selects this tool over the other dataset_* tools. It lacks any negative guidance or named alternatives, but the trigger condition is unambiguous.

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