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

Beyond the readOnly/idempotent annotations, the description adds valuable context: derived from CF analytics-engine, no PII, cached 5min-1h depending on window. This informs the agent about data freshness, privacy, and aggregation source, which annotations do not cover.

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 well-structured and front-loaded with the core action, then lists returns, use cases, and implementation details. Every sentence adds 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?

With one simple parameter, strong annotations, and no output schema, the description covers return content (pack, tool, count), use cases, caching, and privacy. It provides sufficient information for an agent to select and invoke 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?

Schema coverage is 100% with a clear enum and default for 'window'. The description repeats the window options and adds caching behavior tied to the window, but this is more behavioral context than parameter semantics. Baseline of 3 is appropriate since the schema already documents the parameter fully.

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: returns top tools, packs, and total call volume over a window. It also explains the aggregation source and explicitly distinguishes it from mere search by focusing on what other agents are calling, which differentiates it from sibling tools like discover_tools.

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?

Provides three explicit use cases (discovering hot data sources, confirming canonical choice, aligning with agent needs) and explains window tradeoffs. However, it does not name alternative tools or state when not to use it, so it falls slightly short of the highest bar.

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