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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It explains that the tool 'analyzes event performance, perk utilization, and customer engagement' and 'suggests optimizations,' which gives a sense of the internal logic. However, it does not state whether the operation is read-only, whether special permissions are needed, or what the return structure looks like, leaving some gaps.

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 two sentences, front-loaded with the primary purpose and followed by a concise list of analysis areas. Every sentence contributes value, with no filler or redundancy.

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 one parameter, no output schema, and no annotations, the description is reasonably complete. It conveys what the tool does, the key analysis areas, and the nature of the output (data-driven recommendations). It could be improved by explicitly mentioning the return type (e.g., a report or score), but the current level covers the essentials.

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 covers 100% of the single parameter brandId with a simple description, so the parameter is already well documented. The description adds no additional meaning about brandId beyond what the schema provides, warranting the baseline score of 3.

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's function: 'Get a program health assessment for a brand with data-driven recommendations.' It specifies the resource (brand health) and the output (recommendations), and the second sentence enumerates the analysis dimensions (event performance, perk utilization, customer engagement), distinguishing it from sibling brand tools like brand_analytics or brand_info.

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 implies when to use the tool (when a brand health assessment with recommendations is needed), but it does not explicitly state when NOT to use it or name alternatives. Sibling tools exist, but no guidance is given on how this differs from brand_analytics or brand_rankings, leaving some ambiguity.

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