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

With no annotations, the description carries the full burden; it does so by explicitly framing the operation as a read ('Read this') and enumerating the exact information returned: source, date computed, licence, and citation. It does not detail output formatting, but that is minor for a parameterless metadata 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 sentences with no filler; the core content is front-loaded and every clause serves the purpose. It is an ideal length for a trivial parameterless tool.

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?

Without an output schema, the description sufficiently explains the returned information. It could mention whether the result is a formatted string or structured fields, but for provenance attribution the stated content list is enough for correct tool selection and invocation.

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 parameters and 100% schema description coverage, so there is nothing to explain at the parameter level. The description still adds value by clarifying what the output fields will be.

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?

Clearly identifies a specific resource (the Orgbix dataset) and the content it provides: source, computation date, licence, and citation. The phrase 'attribute a figure correctly' gives a concrete use case that separates it from sibling tools focused on columns, rows, stats, and search.

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' explicitly tells the agent when to invoke this tool. It does not explicitly list exclusions, but none of the sibling tools overlap with citation/provenance duties, so the guidance is sufficient.

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