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

No annotations are provided, so the description carries the full disclosure burden. The word 'Read' implies a non-mutating lookup with no side effects, which is the key behavioral fact for a zero-parameter tool, but read-only status is never stated explicitly and no auth, rate-limit, or scope caveats are mentioned.

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 in the first sentence and the usage cue follows, so an agent scanning the first line already knows what it gets.

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?

With no output schema, the description must convey the return payload, and it does so by enumerating source, computed date, licence, and citation. It stops short of describing the shape or format of the citation field, but that is a minor omission for a fixed-metadata endpoint.

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 this is a baseline 4. There is no parameter semantics to explain, and the description correctly does not invent any.

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 resource (provenance metadata for the Working Capital Quotes dataset) and enumerates the four things it returns: source, compute date, licence, citation. The verb is implicit ('read this') rather than stated, but the scope is unambiguous and clearly distinct from siblings like dataset_stats or dataset_columns.

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 the agent. It does not name alternatives (e.g. dataset_columns for structure), but for a zero-parameter metadata lookup the use case is narrow enough that no exclusions are needed.

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