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

Beyond the readOnly/idempotent annotations, the description discloses that data comes from the customer's Google or Microsoft tenant, the specific output dimensions, and that it returns nothing unless app discovery is enabled. This is meaningful behavioral context the annotations do not provide.

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?

Three sentences cover the tool's output, its strategic purpose, and an important empty-result caveat, with no filler. The most relevant identification is front-loaded and every sentence 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 no-parameter, read-only listing tool, the description covers what the data is, where it comes from, what fields are included, and when it returns nothing. Even without an output schema, an agent has enough to call and interpret this tool correctly.

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 tool has zero parameters and an empty input schema, so there are no parameter semantics to clarify. With no parameters, the baseline 4 applies and the description does not need to compensate for schema gaps.

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 defines precisely what the tool returns—tenant-discovered SaaS apps, distinct user counts, and lure availability—and frames it as input for deciding what to simulate next. It clearly distinguishes this from sibling tools like get_program_summary or list_campaigns by naming its role in simulation selection.

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 explicitly positions itself as 'the single best predictor' and 'the right input when suggesting what to simulate next,' giving strong context for when to call it. It does not name excluded cases or alternative tools, so it stops short of full when/when-not guidance.

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