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

Annotations already indicate read-only, open-world, idempotent, and non-destructive. The description goes beyond this by disclosing that the data is self-aggregating from CF analytics-engine, contains no PII, and is cached 5min-1h depending on window. These are valuable behavioral traits not present in the structured hints.

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 front-loaded with the tool's purpose, follows with a structured use-case list, and ends with key data characteristics. Each sentence earns its place, and the length is proportionate to the tool's simplicity. There is no redundancy or fluff.

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 read-only tool with one optional parameter and no output schema, the description is complete: it specifies the return contents (top tools, top packs, total call volume), explains when to use it, and covers caching, data source, and PII. No significant gaps remain.

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 fully describes the single optional 'window' parameter with an enum and semantic explanation. The description mentions the window options but does not add any new meaning beyond what the schema already provides. With 100% schema coverage, baseline 3 is appropriate.

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 opens with a clear statement of what the tool returns (top tools, packs, call volume over a window) and explicitly names three use cases, making it easy to distinguish from sibling tools like discover_tools. The verb 'Returns' and resource 'trending calls' are specific and unambiguous.

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 'Useful for' list provides three concrete scenarios (discovering hot data sources, confirming canonical tool choice, and assessing alignment with agent needs), giving a clear sense of when to use it. However, it does not mention when not to use it or explicitly name alternatives, so it stops short of a 5.

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