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

Annotations already declare read-only and idempotent, but the description adds context: data provenance ('derived from CF analytics-engine'), privacy ('no PII'), and caching ('Cached 5min-1h'). This goes beyond the structured hints.

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—two sentences plus a numbered list—and every sentence adds value: definition, use cases, data source/caching. It is front-loaded with the core function.

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 no output schema, the description specifies the return content ('top tools, top packs, total call volume', 'pack, tool, count') and explains the window behavior. Caching and use cases round it out. The tool's simplicity (1 optional param) allows this to be sufficient.

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 input schema already covers the single `window` parameter with a full description, and the tool description also mentions the window options in the first sentence. Per the rubric, high schema coverage yields a baseline of 3; the description contributes no additional parameter semantics.

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 identifies the exact resource ('the top tools, top packs, and total call volume') plus the window options. It clearly distinguishes from siblings like discover_tools by focusing on current usage trends among AI agents.

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?

It provides three explicit 'Useful for' scenarios, telling agents when to invoke it—discovering hot data sources, confirming canonical tool choice, and checking alignment. It does not name alternatives or explicitly state when not to use it, so it falls slightly 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.