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

Beyond the safety hints in the annotations, the description provides deep behavioral context regarding data capture methodology. It highlights critical operational gaps, such as 'native crashes... never reach Glitchless,' and how Glitchless defines 'a stopped game loop' versus a standard store 'users in a day.'

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 necessarily long due to the complexity of comparing two disparate data sources and listing multiple metrics for two platforms. However, it is dense with value, and the most critical comparative information 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?

Given the complexity and the high level of detail in the schema and annotations, the description is exceptionally complete. It clarifies the data limitations and the specific value proposition of the tool.

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?

Although schema coverage is 100%, the description adds crucial operational context to the parameters. For instance, it notes that 'Google Play reports a day or two late' regarding slow rendering, adding vital temporal context to the `hours` parameter definition.

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 excels at stating its purpose by defining the relationship between store-reported vitals and Glitchless metrics. It explicitly differentiates its scope by listing specific metrics for Google Play (e.g., slow rendering, ANR) versus the App Store (e.g., hang rate, p90/p50).

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 guidance by telling the agent exactly *how* to apply the data: 'Use it to tell whether a problem players report is one Glitchless can see, and which devices to test on.' This defines the primary decision-making context for the tool.

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