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

With no annotations, the description carries the full behavioral burden; it discloses the returned content but says nothing about read-only safety, whether the values are static or recomputed, caching, or freshness guarantees. For a zero-parameter metadata lookup the risk is low, so the implicit read of static metadata is mostly self-evident, but the disclosure is 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 tight sentences with no filler; the payload (what fields come back) is front-loaded and the usage cue follows it.

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 and no annotations, the description partially compensates by listing the fields a caller receives, which is the key information for an attribution task. It stops short of naming exact field names or return format, but nothing essential for invoking a zero-arg tool 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?

There are zero input parameters, so the baseline is 4; the description correctly adds no parameter guidance because none is needed.

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 returns — source, computation date, licence, and citation for the Stocktaka dataset — which is highly specific and clearly distinct from the data-oriented siblings (columns, row, stats, top). It lacks an explicit verb (e.g., 'get'/'return'), so it reads as a noun phrase rather than a stated action, but the resource and its contents are unambiguous.

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 situational trigger for calling the tool. No alternatives or exclusions are named, but given that no sibling provides provenance or citation data, there is no real ambiguity to resolve.

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