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

With no annotations, the description carries the full disclosure burden and does so reasonably: it lists the exact metadata returned (source, computed date, licence, citation), implying a side-effect-free read. It omits any auth/permission notes, but for a zero-parameter metadata fetch the main behavioral question — what comes back — is answered.

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 the returned fields front-loaded and the actionable instruction second. No filler or redundancy; every clause carries information.

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 is obligated to describe what is returned, and it does so completely. The only gap is the hardcoded "Contractor Lead Quotes dataset", which leaves unclear whether this tool is scoped to a single dataset or generalizes across the dataset_* family.

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 schema defines zero parameters (100% coverage of an empty object), so per the baseline there is nothing for the description to explain. No misleading input assumptions are introduced.

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 surfaces (source, compute date, licence, citation) for a named dataset, which is more concrete than the title alone. It does not name or differentiate from any sibling, but no sibling appears to overlap with provenance/attribution content, so confusion risk is low.

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 clear triggering context for use. It stops short of explicit exclusions (e.g. “for column definitions use dataset_columns”), but the purpose is narrow enough that the intended usage is unambiguous.

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