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

Annotations already mark it as read-only, idempotent, and non-destructive, but the description adds valuable context: it is self-aggregating, derived from CF analytics-engine, contains no PII (only pack/tool/count), and is cached for 5 minutes to 1 hour depending on the window. These details go beyond annotations and fully disclose behavior.

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 output, followed by a concise numbered list of use cases and a brief technical note. Every sentence adds value, and the length is appropriate for the tool's richness.

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?

Despite lacking an output schema, the description fully specifies the return contents (top tools, top packs, total call volume, and (pack, tool, count) tuples), the caching behavior, and the absence of PII. With only one optional parameter fully documented, this is complete for an agent to use 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?

The schema covers 100% of parameters, including a detailed enum description explaining the trade-off between shorter and longer windows. The tool description merely repeats the window options without adding new semantic meaning, so it does not go beyond what the schema already provides.

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 what the tool does: it returns top tools, top packs, and total call volume over a recent window, aggregating what other AI agents are calling. It distinguishes itself from siblings by focusing on real-time agent usage trends rather than static discovery or recent changes.

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 explicit use cases with numbered examples (discovering hot data sources, confirming canonical tools, aligning use cases). It does not state when not to use it or name alternative tools, but the guidance is specific enough to help an agent decide when to invoke it.

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