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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the semantics of each output metric, states the default (90 days) and maximum (365 days) range, and instructs to 'Label exposure as estimated.' It also hints at timezone sensitivity by referencing Asia/Shanghai. This goes beyond a simple action statement and gives useful behavioral context.

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 moderately sized but well organized: it opens with the core purpose, defines the three metrics, then provides usage notes. There is no fluff or repetition; every sentence adds value. It is not overly wordy for the information it conveys.

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 only three parameters and no output schema, the description covers the essential decision points: metric definitions, how to request historical vs period data, and range constraints. It lacks explicit details about response structure, but since there is no output schema, that may be acceptable. The description is sufficiently complete for an agent to call the 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 input schema already covers all three parameters with descriptions (100% coverage), so the baseline is 3. The description adds practical guidance on how to choose date_from/date_to for different use cases (e.g., historical snapshot vs period total) and clarifies the default and maximum date range, which helps an agent select correct parameter values beyond the schema's basic format.

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 states a specific verb and resource: 'Get daily and cumulative estimated exposure across all Android/iOS packages associated with a game.' It explicitly defines three distinct metrics (daily_impressions, snapshot_impressions, daily_impressions_total) and scopes the operation to all packages, making it clearly distinguishable from sibling tools that cover other game metrics like delivery or trend.

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 scenario-based usage guidance for metrics: 'For historical total exposure with no date, query yesterday in Asia/Shanghai and use its snapshot_impressions' and 'For the past N days, use daily_impressions_total.' However, it does not explicitly compare this tool against alternative tools or state when to prefer this over a sibling, leaving tool-selection guidance implicit.

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