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

Annotations already cover safety (readOnly, idempotent, non-destructive), so the description's real contribution is the aggregation semantics: top-level totals are the sum across live numbers, and the `numbers[]` array carries the per-number detail. That is non-obvious behavior an agent would otherwise get wrong when interpreting results. It does not mention auth scope or staleness of the numbers, which keeps it from a 5.

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?

Three short paragraphs: what it returns, the aggregation caveat, then the alternative. The quota example and the 99% warning both earn their place by preventing misreading of the totals. Nothing is repeated from the title or annotations.

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?

An output schema exists, so return values need no explanation, and there are no parameters to document. The remaining risk — misinterpreting org totals as per-number quota — is addressed head-on, which is exactly what a zero-argument aggregate tool needs.

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?

The tool takes zero parameters, so there is no schema surface for the description to compensate for. The description correctly spends no words on inputs and instead explains how the response is partitioned.

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 specific verb and resource ("Get message and contact usage") and pins the scope to the whole organization across every live number. The distinction from the per-number siblings is made concrete by the quota-doubling example, so an agent can identify this tool without opening any schema.

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?

Explicitly routes the agent: use this for the org-wide picture and the `numbers[]` breakdown, and use `get_message_quota` or `get_contact_quota` when only one number matters. It also gives the condition that makes this tool necessary — a healthy total can mask a number at 99%.

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