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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, which the description does not contradict. Beyond that, the description adds valuable behavioral context: it explains the data source ('derived from CF analytics-engine'), privacy guarantees ('no PII, just (pack, tool, count)'), and caching behavior ('Cached 5min-1h depending on window'). This goes well beyond what annotations provide.

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 well-structured and front-loaded with the core purpose, followed by actionable use cases and important technical caveats. Each sentence contributes meaningful information—no fluff or redundancy. The use of bullet points improves readability without being overly verbose.

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?

Even though there is no output schema, the description explicitly states the return contents: top tools, top packs, total call volume, and the data format '(pack, tool, count)'. It also mentions caching and the absence of PII, which addresses common concerns. For a tool with one optional parameter, the description provides sufficient context to select and invoke it correctly.

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 schema already covers the single parameter 'window' fully with an enum and a description ('24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.'). The tool description repeats the window values but adds no new parameter semantics beyond what the schema explains. Thus, the baseline of 3 is appropriate given the high 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 the tool's function: it returns top tools, top packs, and total call volume over a recent window, with examples of the window values. This specific verb and resource clearly distinguishes it from siblings like discover_tools or ask_pipeworx, which focus on discovery or direct querying rather than aggregated trending signals.

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 description provides explicit 'Useful for' bullets that give concrete scenarios: discovering hot data sources, confirming a canonical tool choice before asking, and checking alignment with other agents' needs. While it does not name alternative tools or say 'when not to use', the usage contexts are clear and actionable, giving strong practical guidance.

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