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

With no annotations provided, the description carries the behavioral burden. It signals a read-only action ('Read this') and enumerates the exact fields returned, which is meaningful transparency for a metadata lookup tool. It does not discuss output formatting, but that is a minor gap for such a simple tool.

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 contain all essential information, with the data fields front-loaded and the usage guidance following immediately. There is no redundancy or filler.

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 is complete: it identifies the dataset, lists the returned provenance fields, and explains why an agent should invoke it. No critical information is missing.

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 schema already fully covers parameter semantics. The description adds no parameter information, but none is needed; the baseline of 4 is appropriate.

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 clearly states a specific purpose: returning the source, computed date, licence, and citation for the Offdayly dataset. This distinguishes it from the sibling dataset tools, which focus on columns, rows, search, stats, and comparisons.

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?

The instruction 'Read this to attribute a figure correctly' explicitly identifies when the tool should be used. It does not name alternative tools or exclusion conditions, but the zero-parameter provenance scope makes the intended use unambiguous.

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