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

Annotations already establish that this is a safe, read-only, idempotent operation, and the description adds useful context about what the policy reveals, including approval behavior and daily cap usage. It does not discuss auth, rate limits, or return formatting, but with strong annotation coverage that is a minor gap.

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 core purpose and every listed item corresponds to a policy component the agent needs. It is somewhat long and list-like in a single sentence, but not redundant.

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 return content, and it enumerates the policy components thoroughly. For a zero-parameter read tool with rich annotations, it provides enough context for an agent to call it confidently before drafting.

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?

There are zero parameters, so there is nothing for the description to clarify beyond the empty schema. Per the zero-parameter baseline, a 4 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 uses a specific verb/resource and fully explains what policy data is returned: approval setting, agent mode, daily send caps, blocked domains, and non-mailable statuses. It is clearly distinguishable from sibling tools such as draft_email or simulate_campaign because it is the policy-reading tool.

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?

It gives a clear usage condition: "Read it before planning a batch of drafts." It does not name explicit alternatives or when-not cases, but the sibling set contains no direct policy-read alternative, so the guidance is sufficiently clear.

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