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

With no annotations, the description carries the burden and does disclose useful traits: the math is deterministic, an LLM is not involved, and the response includes per-metric health tags, an overall grade, and a ranked focus list. It does not state whether inputs are persisted or whether results are cached/reproducible across versions, which is the remaining gap for a compute tool.

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 dense sentences with the input contract first and the output list second; every clause adds information and there is no filler.

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?

No output schema exists, so the description must enumerate the return value, and it does so concretely (metric set, health tags, grade, ranked focus list). What remains thin is input-side detail — units and the meaning of the optional-but-unnamed fields — for a 10-parameter tool.

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 for all 10 params. It names the input families (MRR, customers, churn, CAC, margin, growth), which maps loosely to cac/churned_mrr/gross_margin_pct/new_mrr, but leaves prior_mrr, expansion_mrr, new_customers, and sales_marketing_spend unexplained and gives no units (dollars vs. percentage for gross_margin_pct).

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?

States a concrete verb-and-resource pair: take MRR/customer inputs and compute a specific named set of SaaS metrics (ARR, ARPA, LTV, CAC, payback, Rule-of-40, NRR, GRR) plus a grade and a ranked fix list. Nothing else in the sibling list does anything similar, so an agent can select it unambiguously.

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?

Tells the agent exactly what to supply ('POST your MRR/customers') and flags that churn/CAC/margin/growth are optional, which frames the required-vs-optional call. It gives no when-not-to-use conditions or pointer to an alternative, but no sibling tool overlaps this computation.

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