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

Annotations already establish this as a safe, idempotent, closed-world read. The description adds real behavioral context beyond them: the calculation is deterministic arithmetic with every step and assumption exposed, and the output is an estimate of jobs at stake rather than a forecast of recovered revenue. Disclosing that the response includes Ringback price coverage is extra transparency the annotations cannot convey.

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 formula is front-loaded and each sentence does work: calculation, then the estimate-vs-prediction caveat, then the Ringback price-coverage output. The third sentence leans slightly promotional and could be folded into the output description, but it is short and informative rather than wasteful.

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, the description carries the burden of explaining returns, and it does: per-week, per-month and per-year figures, shown steps and assumptions, plus a break-even framing against Ringback's price. It stops short of describing the response structure or formatting, which is a minor gap for a purely arithmetic 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 coverage is 100%, so the baseline is 3, but the description earns an increment by stating how the four inputs combine (calls per week x missed % x close rate x average job value), which tells the agent the interaction semantics rather than just each parameter in isolation. Units, ranges and examples are already handled by the schema.

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 gives a specific verb and resource (arithmetic on the caller's own numbers producing a missed-call cost estimate) and spells out the exact formula and the periods returned. It also explicitly bounds the claim — value of jobs at stake, not a recovery prediction — which cleanly separates it from the sibling tools that report actual calls or texts.

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?

Usage context is clear: the caller must supply their own business numbers, and the result is a what-if estimate rather than a measurement. It does not, however, name when to prefer this over investigating real data via my_missed_calls, leaving that routing to inference.

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