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

Beyond the readOnly/idempotent annotations, the description reveals that data is derived from CF analytics-engine, contains no PII, exposes only (pack, tool, count) tuples, and is cached 5min-1h depending on window. These are meaningful behavioral details that shape expectations.

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 information-dense: it leads with the core value proposition, follows with a scannable numbered use-case list, and closes with essential notes on data source and caching. Every sentence contributes useful content without redundancy.

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 covers the return shape (pack, tool, count), the data source, privacy guarantees, caching, and practical use cases. For a simple read-only tool with one optional enum parameter, this is complete.

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 only parameter 'window' is fully described in the schema with enum values, a default, and guidance on shorter vs longer windows. The description restates the window options but adds no further parameter-level meaning, so the baseline of 3 applies given 100% schema coverage.

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: returns top tools, top packs, and total call volume over a recent window. It distinguishes itself from sibling tools by focusing on aggregate usage trends ('what other AI agents are calling') rather than generic 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?

A 'Useful for' list provides three concrete scenarios: discovering hot data sources, confirming a canonical tool choice, and checking alignment with agent needs. It does not explicitly name alternative tools or state when not to use it, but the clear context is enough for an agent to decide.

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