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

Beyond the annotations (readOnly, idempotent, non-destructive), the description adds valuable context: it is self-aggregating, derived from CF analytics-engine, contains no PII, and is cached 5min-1h depending on window. This goes well beyond the structured annotations and sets clear expectations about freshness and privacy.

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 efficient: a one-sentence summary, a list of concrete use cases, and a brief note on data provenance and caching. Each sentence earns its place, and the bullet-like structure aids scanning without unnecessary verbosity.

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?

Given the tool's simplicity (one optional parameter, no nested objects) and strong annotations, the description covers all essential aspects: return contents ('top tools, top packs, total call volume'), output shape ('(pack, tool, count)'), data source, caching, and privacy. This is sufficient for an agent to select and invoke the tool 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?

Schema coverage is 100% for the single 'window' parameter, and the schema already explains the meaning of each window option. The description repeats the window values but adds no new parameter-specific semantics; the caching detail is behavioral rather than parameter-level, so the baseline of 3 is appropriate.

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 with a specific verb ('Returns') and resource ('top tools, top packs, and total call volume'), and opens with a concrete question ('What other AI agents are calling on Pipeworx right now'). This distinguishes it from siblings like discover_tools, which focus on discovery rather than aggregated usage trends.

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 use cases ('Useful for: ...'), including discovering hot data sources, confirming canonical choices, and aligning with agent needs. However, it does not explicitly mention when not to use it or name alternative sibling tools, so it stops 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.