validate_unit_economics

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

With no annotations provided, the description carries the full burden, and it does so thoroughly. It discloses the exact return shape, the severity model with consequences for each severity class, the validation rules enforced, and the deterministic nature ('No LLM calls').

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?

Four dense, information-rich sentences cover purpose, validation rules, return format, action guidance, and determinism. Every sentence earns its place and there is no filler or repetition of schema content.

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?

The description is sufficiently complete for a validation tool with no output schema: it specifies the return structure and behavioral outcomes. The main minor gap is lack of explicit per-parameter clarification for optional fields, but the overall context is strong enough for an agent to invoke the tool correctly.

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 0%, so the description must compensate. It meaningfully explains the roles of customers, ARPU, annualRevenue, LTV, and CAC through the stated formulas and validation rules. However, grossMargin and monthlyChurn are only indirectly inferable from 'CAC payback bounds' and 'cohort-positivity check', leaving their exact semantics partially unspecified.

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 tool's purpose with a specific verb ('Sanity-check') and resource ('a unit-economics row'), and places it in a concrete context ('before publishing it in a business-model slide'). It also distinguishes itself from the sibling compute_* tools by focusing on validation rather than signal computation.

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 description gives clear usage context ('before publishing') and defines the decision flow: block-severity errors require regeneration, warn-severity errors only need surfacing. It does not explicitly name alternatives or when not to use it, but the validation-vs-computation distinction is strongly implied by its content.

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