get_email_preferences

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

With no annotations, the description carries the behavioral burden. It adds useful context by explaining that the tool surfaces opt-out preferences and warns against mistaking them for delivery failures. However, it does not explicitly state that the operation is read-only, nor describe error/empty-result behavior.

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 sentences, zero filler. The core meaning is front-loaded, and the second sentence earns its place by providing a concrete diagnostic heuristic that clarifies when the tool matters.

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?

For a single-parameter read tool with no output schema, the description is nearly complete: it states what it returns and when to call it. It could add a little more about interpreting the returned preferences or explicitly naming the setter sibling, but the essential invocation context is present.

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 schema has 0% description coverage, so the description must compensate, but it never mentions the email parameter or says that the person is identified by email address. The phrase 'one person' only loosely implies the selection mechanism, leaving the agent to infer that the required 'email' property is the identifier.

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 clearly identifies this as the tool that retrieves one person's email-receipt preferences, using the name plus a succinct noun-phrase. The diagnostic framing — an opt-out looks identical to a delivery failure — differentiates it from siblings like set_email_preferences and list_suppressions, so an agent knows exactly what it returns.

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 second sentence gives explicit when-to-use guidance: check this before asking a human why someone isn't receiving email. It does not name sibling tools or give explicit when-not-to-use exclusions, but the context is clear and actionable.

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