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

No annotations are provided, so the description carries the behavioral burden. It discloses the return contents (source, date, licence, citation) and implies a read-only lookup, but does not explicitly state side-effect absence, authentication needs, or output format.

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, front-loaded with what is returned and followed by the use case. Every phrase earns its place with no redundant repetition.

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?

For a zero-parameter metadata lookup with no output schema, the description fully explains the returned fields and the appropriate use. No critical information appears missing for an agent to select and call this tool correctly.

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 has zero parameters, so the rubric baseline is 4. The description adds no parameter detail because none exist; schema description coverage is 100% but moot here.

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 ('Commercial Refinance Quotes dataset') and enumerates the metadata returned: source, computation date, licence, and citation. It is clearly distinct from sibling dataset_* tools that query rows or statistics, though it lacks an explicit verb like 'returns' to reach the top mark.

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?

It states when to read it: 'to attribute a figure correctly.' That gives clear context, but it does not name alternatives or say when not to use it (for example, use dataset_row or dataset_search for data values).

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