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

With no annotations, the description carries the burden of explaining behavior. It implies a read-only operation and that it returns counts, but does not define what 'active' means, whether the counts are network-wide or user-specific, or how the output is structured. This is acceptable for a simple stats tool but leaves some ambiguity.

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, front-loaded sentence that states the primary behavior first and then adds practical context. Every word earns its place, with no fluff or repetition.

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 zero-parameter, no-output-schema tool, the description is largely complete: it explains what is returned and why it is useful. However, it could be more precise about the scope (e.g., whole network) and the meaning of 'active', which would improve completeness.

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 the schema is empty (100% coverage). The description adds value by explaining what the returned counts represent (brands, users, rewards), which is meaningful context beyond the schema. A baseline of 4 for no-parameter tools is appropriate, and the description fulfills this.

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 returns counts of active brands, users, and rewards, using a specific verb ('Returns') and a resource ('how many brands, users, and rewards are active'). This distinguishes it from sibling tools like network_analytics or network_info by focusing on aggregate counts and scope.

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 provides a clear use case: 'useful for understanding the scope of available deals and cashback offers.' This implies when to use the tool (for quick scope assessment), though it does not explicitly mention when not to use it or name alternatives.

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