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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered and the bar for the description is lower. The description usefully adds that the calculation is configurable (optional discounting, LTV-to-CAC ratio), but says nothing about precision, currency conversion behavior, or what the returned figure represents. Adequate but not rich.

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?

A single well-packed sentence with the verb and domain front-loaded and the optional features trailing. No filler and nothing redundant with the schema. It is dense enough that a reader must parse carefully, but every clause carries information.

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 seven parameters, the description should say more about what comes back — the LTV figure, whether the LTV:CAC ratio is returned, currency handling, and the discounting convention. It signals these capabilities exist but leaves the agent guessing about the result shape. Adequate for invoking the tool, incomplete for interpreting it.

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?

Schema description coverage is only 43%, so the description carries real weight here, and it does: it names average order value, purchases per year, lifespan, margin, optional discounting and the LTV-to-CAC ratio, which accounts for six of the seven parameters conceptually. Only 'currency' (a display-only, defaulted field) is left unaddressed, and no units or formula for the discount rate are given.

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 opens with a specific verb and resource ('Calculates customer lifetime value') and narrows the domain to repeat-purchase businesses such as D2C and ecommerce. It enumerates the input drivers (AOV, purchase frequency, lifespan, margin) plus optional extensions, so the tool's function is unambiguous. It does not explicitly contrast itself with adjacent siblings like unit_economics, max_affordable_cac or ecommerce_metrics, so it stops short of full differentiation.

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?

Applicability is implied by 'for repeat-purchase businesses such as D2C and ecommerce', which tells the agent the model assumes recurring purchases rather than one-off or subscription contexts. However, there is no explicit when-to-use statement, no exclusion (e.g. 'not for SaaS/subscription churn models'), and no named alternative such as max_affordable_cac for pure CAC work. Usage must be inferred.

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