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

Annotations already declare readOnlyHint=true and non-destructive behavior. The description adds significant context beyond those hints: data provenance ('derived from CF analytics-engine'), privacy ('no PII'), the exact aggregation granularity ('just (pack, tool, count)'), and caching behavior ('Cached 5min-1h depending on window'). This fully informs the agent of the tool's operational traits.

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 with a front-loaded purpose statement, a clear return summary, and contextual details. The 'Useful for' list adds length but each bullet provides actionable guidance, so no sentence is wasted.

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 having no output schema, the description fully explains what the tool returns (top tools, top packs, call volume), how to choose the window, why it's useful, and its provenance/privacy/caching behavior. For a simple single-parameter read-only tool, this is exceptionally complete and self-contained.

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 entirely covers the single `window` parameter with an enum and a description explaining trade-offs between windows. The tool description mentions the window options but adds no new semantic information beyond what the schema already provides, so the baseline score 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 opens with a clear, specific statement of what the tool does: 'What other AI agents are calling on Pipeworx right now' and lists the concrete outputs (top tools, top packs, total call volume). It distinguishes itself from sibling tools like discover_tools by focusing on aggregated usage patterns rather than general tool 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?

An explicit 'Useful for' section provides three concrete use cases, from discovering hot data sources to confirming canonical tool choice. However, it doesn't name alternative tools or state when not to use this tool, falling just short of perfect guidance.

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