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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds real behavioral value beyond those: the result-set sparsity behavior (often empty despite engagement) and the implied expectation that nonzero interactions are needed. This changes how an agent should handle the response, which qualifies as meaningful behavioral disclosure.

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 purpose is front-loaded in sentence one, and the behavioral caveat follows in sentence two. Every word earns its place.

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 tool with 15 parameters including four enums, date filters, multiple numeric bounds, and a cursor-based pagination field, plus no output schema, the description is far too thin to fully prepare an agent. It covers the 'what' and the sparsity caveat but says nothing about filtering behavior, pagination, or response shape, leaving substantial room for mis-invocation on a complex input surface.

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?

Schema description coverage is 0% and there are 15 parameters, so the description carries the full burden of explaining parameter intent — but it mentions none of them. The only implicit signal is that subscriberHash and projectId identify 'one member' and her campaigns. Date filters, sorting semantics, opened/clicked filters, time-to-open/click bounds, and pagination are all left to the agent to infer from bare names.

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 and resource ('List the individual campaigns one member received, opened or clicked') and characterizes it as an 'email-level drill-down', which clearly separates it from aggregate sibling tools like sm_get_insights_advanced and member-profile tools like sm_get_member. It states both the scope (one member) and the grain (individual campaigns) explicitly.

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 conveys clear usage context by warning that this drill-down 'is often empty even for highly engaged members' and instructs the agent to 'always keep a fallback' — actionable guidance for call planning. It does not, however, explicitly name alternatives or state when NOT to use this tool, so it stops short of the top of the scale.

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