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

Annotations already establish the tool as read-only, idempotent, and non-destructive. The description adds valuable context: derived from CF analytics-engine, no PII, cached 5min-1h depending on window, and the self-aggregating nature. This goes beyond what annotations convey.

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 composed of four dense but purposeful sentences: returns, use cases, data source/privacy, and caching. Every sentence earns its place and the structure is easy to scan. Slightly verbose but not wasteful.

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 tool with one optional parameter and no output schema, the description covers the return values, use cases, data provenance, privacy, and caching. The only gap is a precise output format, but it hints at (pack, tool, count). Overall very 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 'window' parameter is fully documented in the schema with an enum and description explaining short vs. long window trade-offs. The description repeats the window options without adding new parameter-level details, so it adds little beyond the schema.

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 it returns top tools, top packs, and total call volume over a recent window, with a specific focus on what other AI agents are calling. This distinguishes it from sibling tools like discover_tools by emphasizing real-time trend aggregation rather than static discovery.

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 concrete scenarios: discovering hot data sources, confirming canonical tools, and checking alignment with agent needs. This gives clear when-to-use context, though it doesn't explicitly name alternative tools or exclusions.

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