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

The description goes beyond the readOnlyHint by explaining the aggregation source (CF analytics-engine), explicit no-PII guarantee, the output shape (pack, tool, count), and cache duration (5min–1h). No contradiction with annotations.

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 compact yet rich: the first sentence defines the output, the second lists concrete use cases, and the third covers data provenance and caching. No redundant phrasing.

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 aggregate tool with a single optional parameter, the description covers output content, temporal windowing, caching, and privacy. The output schema is absent, but the description specifies the return elements (pack, tool, count), making it self-contained.

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 schema already fully documents the 'window' parameter with enum and semantics. The description reinforces the distinction between short and long windows and adds that cache varies by window, providing extra context beyond the schema.

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 returns trending AI agent calls (top tools, top packs, call volume) over a 24h/7d/30d window. It differentiates from siblings like discover_tools by focusing on aggregate usage signal rather than tool lookup.

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' section explicitly lists three scenarios that guide an agent on when to call this tool, covering hot data discovery, canonical tool confirmation, and use-case alignment. It doesn't name specific alternatives or exclusions, but the context is clear and actionable.

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