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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds valuable behavioral context beyond these: it reveals the data source ('derived from CF analytics-engine'), privacy handling ('no PII'), the exact output granularity ('just (pack, tool, count)'), and caching behavior ('Cached 5min-1h depending on window'). This fully discloses the tool's internal behavior and data provenance, exceeding the annotation baseline.

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: the first sentence immediately states what the tool returns, followed by a numbered list of use cases, and ends with technical notes on data source and caching. Every sentence contributes unique value with no filler. It is appropriately sized for a tool with this level of nuance.

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?

The tool is simple (one optional parameter, no output schema) and the description covers all essential aspects: return value (top tools, top packs, total call volume), data source, privacy, caching, and usage context. Since there is no output schema, the description reasonably conveys the output structure by mentioning '(pack, tool, count)'. The combination of annotations and description provides complete context for an agent to select and invoke this tool.

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' has 100% schema description coverage: the schema already explains the enum values and their semantics ('24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.'). The main description mentions the windows but adds no additional meaning beyond the schema. With high schema coverage, the baseline is 3.

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: 'Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d).' This is a specific verb ('Returns'), resource ('Pipeworx trending data'), and scope ('over a recent window'), and it distinguishes itself from siblings like discover_tools by focusing on current aggregated agent usage rather than general tool discovery. The 'Self-aggregating signal' phrase further differentiates it.

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 three explicit use cases: 'discovering what data sources are hot for current events,' 'confirming a popular tool is the canonical choice before asking your own question,' and 'seeing whether your use case aligns with what most agents need.' These give clear context for when to use the tool. However, it does not name alternative tools or explicitly 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.