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

The annotations already indicate a safe read-only, idempotent, open-world operation. The description adds valuable context beyond that: it explains the data source (CF analytics-engine), states it contains no PII, describes the output shape (pack, tool, count), and discloses caching behavior (5min-1h). This enriches the behavioral understanding substantially.

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 core purpose, then expands into use cases and behavioral specifics. It is slightly longer than strictly necessary, but every sentence contributes meaningful information and the structure is well-organized.

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 simple tool with one optional parameter, rich annotations, and no output schema, the description covers the return values, use cases, data source, privacy, and caching. This is fully sufficient for an agent to decide when and how to use 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?

The input schema provides 100% coverage with a detailed description of the 'window' parameter, including default behavior and the contrast between short and long windows. The tool description does not add further parameter semantics, so the baseline of 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 states exactly what the tool does: it returns top tools, top packs, and total call volume over a recent window. This is a specific verb+resource+output and distinguishes it from sibling tools like discover_tools by focusing on aggregated agent call activity rather than general tool discovery.

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 clear use cases (discovering hot data sources, confirming canonical tool choice, aligning use cases with agent needs) which effectively indicate when to use this tool. However, it does not explicitly name alternatives or state when not to use it, so it falls 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.