validate_unit_economics

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

With no annotations, the description carries the full burden, and it delivers: it names the validation rules, the exact return shape, the severity semantics, and even the deterministic 'No LLM calls' behavior. This gives agents a clear model of what the tool will and will not do.

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?

The description is dense but every sentence earns its place: purpose, checks, return format, severity handling, and determinism. There is no filler or repetition, and the most important scoping information is front-loaded.

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 complex 7-parameter validator with no annotations and no output schema, the description is unusually complete: it states the checks, result shape, and severity policy. The remaining gap is that two named checks ('cohort-positivity' and 'CAC payback bounds') are not fully defined, leaving some ambiguity about how optional parameters factor into them.

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 coverage is 0%, so the description must compensate. It adds real meaning by connecting customers, arpu, and annualRevenue through the math-drift check, and ltv/cac through the ratio floor. However, grossMargin and monthlyChurn are only indirectly implied by 'cohort-positivity' and 'CAC payback bounds', and their exact semantics are not spelled out.

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: 'Sanity-check a unit-economics row before publishing it in a business-model slide.' It then enumerates the exact checks (math drift, LTV/CAC floor, cohort positivity, CAC payback), making the tool's role unmistakable and distinct from the sibling compute_* signal tools.

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?

It clearly states when to use the tool ('before publishing it in a business-model slide') and how to react to results (regenerate on block-severity errors, only surface warning-severity errors). It does not explicitly name alternative tools or spell out when not to use it, so it stops short of a 5.

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