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

Annotations already mark this as read-only/open-world/idempotent, and the description adds substantial behavioral context: it's self-aggregating, derived from CF analytics-engine, contains no PII (only pack/tool/count), and has variable caching (5min-1h depending on window). This goes beyond annotation coverage and helps the agent understand data freshness and privacy implications.

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 with a clear headline, followed by output specifics and a numbered list of use cases, plus a brief data/caching note. Each sentence provides unique value. It is slightly longer than strictly necessary but remains scannable and front-loaded.

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 read-only tool with one optional parameter and no output schema, the description covers what's returned (top tools, top packs, total call volume), how it's derived (no PII), and freshness (caching). No critical gaps remain for the agent to safely invoke and interpret this 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 single parameter 'window' is fully documented in the schema with enum values and default. The description adds meaningful semantics: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This extra insight helps the agent choose the appropriate window beyond just reading the enum.

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 purpose: showing trending tool usage among AI agents, with specific outputs (top tools, top packs, call volume) and time windows. It distinguishes itself from siblings like discover_tools by focusing on trending/aggregate behavior rather than discovery or individual queries.

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 explicitly lists three concrete use cases ('Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice, (3) seeing whether your use case aligns...'), giving clear when-to-use context. It doesn't explicitly state when not to use or name alternatives, but the use cases are highly actionable.

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