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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds one genuinely useful behavioral fact — the figure is 'recomputed weekly' — which tells the agent about data freshness. However, it omits the assumptions underpinning the calculation (annual mileage, fuel price source, whether the premium includes purchase incentives), which materially affect the returned number.

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?

One tight sentence, front-loaded with the metric being computed and ending with the refresh cadence. No filler and nothing redundant with the title.

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, no-output-schema calculator, the description tells the agent what single value comes back and how often it changes, which is close to sufficient. The remaining gap is the underlying assumptions driving the break-even figure, which an agent would need in order to present the result responsibly.

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 takes zero parameters, so the baseline is 4; there is no parameter meaning for the description to add or omit. Nothing in the schema is left ambiguous.

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 names a specific computed metric — the number of miles an electric van needs to recoup its price premium over a diesel equivalent through fuel savings — which is far more precise than the tool name alone. It does not explicitly distinguish itself from related siblings such as get_charging_cost_gap, but the metric itself is distinctive enough that an agent can identify what it produces.

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?

Usage is only implied: an agent can infer this is for EV-vs-diesel total-cost comparisons, but there is no explicit statement of when to call it, when not to, or which sibling to prefer for adjacent questions like charging cost gaps or fleet cost indexes.

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