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

Annotations already declare read-only/idempotent/non-destructive, and the description adds meaningful context: data source (CF analytics-engine), privacy (no PII), and caching behavior (5min-1h). This gives the agent a clear picture of freshness and reliability.

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?

Three sentences, zero wasted words; the 'Useful for' list is compact and actionable. Information is front-loaded with the core function first.

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?

Given the single optional parameter and the absence of an output schema, the description is complete: it states what is returned (top tools/packs/count), the data philosophy, and caching. It covers return value structure sufficiently with '(pack, tool, count)'.

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 describes the 'window' parameter with its default and trade-offs, and the description merely repeats the enum values. Since schema covers the parameter fully, description adds no additional semantic value.

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 identifies the tool as returning trending tool/pack/call-volume data on Pipeworx, with a specific verb ('Returns') and resource ('top tools, top packs, total call volume'). It distinguishes itself from discovery tools by emphasizing it's specifically 'what other AI agents are calling on Pipeworx right now.'

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?

Lists three explicit use cases (hot data sources, confirming canonical tool, checking alignment) that tell an agent when to invoke it. Does not explicitly name alternatives or exclusion criteria, but the use-case framing is strong enough to guide selection.

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