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

No annotations are provided, so the description carries the disclosure burden. It signals a read-only operation ("Read this") and lists the returned metadata, but says nothing about freshness guarantees, whether the licence/citation is stable, or any access requirements. For a zero-parameter metadata lookup the risk surface is small, so this is adequate but thin.

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 list of returned fields comes first and the purpose ('attribute a figure correctly') closes it. The only minor cost is a slightly run-on enumeration of four fields in one sentence.

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 convey the return shape, and it does by naming the four provenance fields. With zero inputs and a simple metadata payload, an agent has enough to call this correctly; a note on citation formatting would be the only plausible addition.

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 no parameters, so there is nothing for the description to disambiguate; the baseline for a zero-parameter schema applies. No param 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 a specific resource (the Crypto Exchange Compare HQ dataset's provenance) and enumerates exactly what it returns: source, computed date, licence, and citation. That is clearly distinct from the numeric/aggregation siblings like dataset_stats or dataset_compare, though it never explicitly contrasts itself 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" implies the trigger condition (you need to cite or attribute a number), which is genuine usage guidance. It does not state when not to use it or reference any alternative, so the guidance stays implied rather than explicit.

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