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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds valuable context: it is self-aggregating, derived from CF analytics-engine, contains no PII, returns only (pack, tool, count) tuples, and is cached for 5min-1h depending on window. This goes well beyond the 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 efficiently organized: a clear opening sentence, a numbered list of use cases, then two final sentences with important caveats. No redundant or excessive wording; every sentence contributes value.

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 one-parameter tool with no output schema, the description fully covers purpose, usage scenarios, data origin, privacy, return payload shape, and caching behavior. The annotations cover safety, so nothing is missing for an agent to successfully invoke and interpret results.

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% and the schema description already explains the window semantics, including the note about shorter vs longer windows. The tool description merely references the window options without adding new meaning, so it meets the baseline of 3 but does not exceed it.

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 uses a specific verb ('Returns') and clearly identifies the resource ('top tools, top packs, and total call volume over a recent window'). It distinguishes from siblings like search_datasets or discover_tools by focusing on aggregated trending usage rather than discovery or search.

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' list provides three concrete scenarios, giving clear context for when to call this tool. It does not explicitly mention when not to use it or name alternative tools, so it narrowly misses a 5.

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