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

With no annotations, the description carries the behavioral burden. It frames the tool as a read operation ('Read this') and lists exactly what information will be provided, implying no side effects. It does not discuss output formatting or access prerequisites, but for a zero-parameter provenance lookup these are minor.

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 compact sentences front-load the returned fields and then state the intended use. No filler or redundancy; every phrase earns its place.

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 simple, no-input provenance tool, the description adequately covers what the agent will get and why it matters. The absence of an output schema is compensated by listing the concrete fields; only exact citation formatting details are left unspecified.

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, and schema coverage is effectively complete, so there is nothing for the description to add about parameter meaning. The description appropriately focuses on the returned content instead of inputs.

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 the resource (Footfally dataset provenance) and the attributes it returns (source, computed date, licence, citation), and 'Read this to attribute a figure correctly' makes the purpose explicit. It is distinguishable from sibling dataset_* tools by its metadata focus, though it lacks an explicit verb like 'returns'.

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 phrase 'Read this to attribute a figure correctly' gives a clear context for when the tool should be used. It does not explicitly name sibling alternatives or exclusion conditions, but the content type is distinct enough that an agent can select it appropriately.

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