get_email_metrics

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

With no annotations provided, the description carries the full behavioral burden. It discloses a non-obvious metric detail: open and click rates are calculated over delivered messages, not sent messages. This is genuinely valuable context that prevents misinterpretation of the reported metrics.

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 short sentences: the first states the tool's function, and the second provides a high-value interpretive nuance. There is no redundancy or filler, and the most important information is front-loaded.

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 tool with no output schema, the description is largely sufficient: it explains what is measured, over what window, and how to interpret a key edge case. It does not spell out the exact return fields, but the named metrics give an adequate mental model.

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 only parameter, 'days', is fully documented in the schema with range and default value, so schema coverage is 100%. The description's 'over a window' aligns with the parameter but adds no additional semantic detail beyond what the schema already states.

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 states the tool reports delivery and engagement over a time window, and the second sentence names open, click, and bounce rates. It does not explicitly differentiate itself from sibling list/get tools, but the aggregate metrics focus is reasonably distinct.

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 gives clear context: use this for delivery and engagement analytics over a window, and it even clarifies how to interpret low open rates with high bounce rates. It does not name alternative tools or explicit when-not-to-use conditions, but the purpose is specific enough to guide selection.

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