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

Annotations already declare readOnly/openWorld/idempotent/non-destructive. The description adds significant non-obvious context: no PII, derived from CF analytics-engine, cached 5min-1h depending on window, and raw output shape (pack, tool, count). This goes well beyond the structured 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?

Every sentence earns its place: definition, outputs, three distinct use cases, and data provenance/cache policy. The purpose is front-loaded, and the structure is tight with no filler.

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 communicates return content (top tools, top packs, volume), data format (pack, tool, count), window options, and caching behavior. For a read-only, simple tool, 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 single parameter `window` is fully documented in the schema with enum values and semantic explanations. The description only repeats the window options without adding new syntax or format details, so baseline 3 applies.

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 opens with a concrete framing ('What other AI agents are calling...') and clearly lists outputs: top tools, top packs, and total call volume. This distinguishes it from siblings like discover_tools by focusing on aggregate usage signals rather than a catalog.

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?

Three explicit use cases are provided (hot data sources, canonical tool confirmation, alignment check), giving clear when-to-use context. However, it does not name alternative tools or state when not to use it, so it falls short of a 5.

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