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

Annotations already establish this as a read-only, idempotent, closed-world calculator. The description adds useful behavioral context: the exact Rule of 40 formula and the fact that extra metrics appear only when their supporting inputs are supplied. It does not contradict annotations.

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 front-loaded with the formula, then the additional metrics, and ends with a reference link. Every sentence earns its place with no fluff.

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 annotations covering safety semantics, an output schema covering return values, and 100% schema description coverage, the description is largely complete. The main gap is not explaining which optional input combinations unlock each additional metric, though the schema compensates for that.

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 100%, so the schema already documents every parameter, including which optional fields feed which metric. The description reinforces the required inputs and names the optional metrics, but it does not add detailed parameter-to-output mapping beyond what the schema provides. Baseline 3 is appropriate.

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 states the exact metric computed (Rule of 40 = revenue growth % + profit margin %) and names the additional optional SaaS metrics it can return. This is specific enough to distinguish it from sibling calculators such as calculate_arr or calculate_churn.

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 implicitly indicates usage through the metric name and the conditional 'when their inputs are given' phrasing, but it gives no explicit when-to-use guidance, prerequisites, or contrast with sibling calculation tools. Adequate but leaves routing to inference.

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