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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds the specific metrics returned and the grouping behavior, which is consistent with the annotations. It does not contradict them and provides additional context about the nature of the response, though it could mention that the operation is non-modifying (already covered by annotations).

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 a single sentence of 25 words that front-loads the core purpose ('Get delivery and engagement analytics'), then enumerates metrics and constraints without redundancy. Every clause adds essential information, making it highly efficient for an agent to parse quickly.

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?

Given that there is no output schema, the description adequately covers what the tool returns (list of metrics), how data can be scoped (account/campaign), and the grouping options. It does not describe the response structure (e.g., JSON format, nesting) or behavior when no data exists, but for a read-only analytics tool with well-defined parameters, this is sufficient for an agent to decide whether to invoke it.

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 input schema has 100% description coverage for its 5 parameters, so the baseline is 3. The description adds semantic value by listing the exact metrics (sent, delivered, opened, etc.) that the tool returns—information not present in the schema—and by clarifying that campaign_id is optional and defaults to account-wide. This helps the agent understand what kind of data to expect beyond parameter definitions.

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 verb 'Get', the resource 'delivery and engagement analytics', and specifies exact metrics (sent, delivered, opened, clicked, bounced, complained). It also defines scope ('account or one campaign') and grouping ('day/week/month'), which distinguishes it from sibling tools like get_deliverability_score or get_monetization_stats that cover different or narrower aspects.

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 explains what data the tool returns and the two scopes (account-wide vs. per campaign), but it does not explicitly contrast with similar tools (e.g., get_deliverability_score, get_monetization_stats) or state when not to use it. The agent must infer usage from the listed metrics and parameters; no exclusion criteria or alternatives are provided.

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