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

With no annotations, the description carries the burden of behavioral disclosure. It divulges that results are aggregated (including unique vs. total counts), which is helpful, but it does not explicitly state read-only behavior, error cases, or aggregation scope. This is adequate but incomplete.

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 tightly packed sentences: the first enumerates the entire result set, and the second gives concrete use cases. No wasted words, and the core purpose 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?

Despite lacking an output schema, the description lists every metric returned scaffolding completeness. The single parameter is fully documented in the schema dropped. Minor gaps like aggregation timeframe or prerequisites do not undermine usability for a low-complexity analytics read.

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 already describes the only parameter as 'Campaign UUID' with 100% coverage. The description adds no additional parameter semantics, so baseline 3 applies.

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 the resource as aggregated stats for one campaign and enumerates the specific metrics returned. It does not explicitly contrast with sibling tools, but the 'one campaign' scope and metric list make its purpose distinct from broader tools like dashboard_summary.

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 provides explicit usage scenarios: answering how a newsletter performed or picking the best-performing campaign. It does not mention exclusions or alternatives, but the stated use cases give clear context for when to invoke it.

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