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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description goes further by revealing it is a self-aggregating signal derived from CF analytics-engine, guaranteein no PII (only pack, tool, count), and explaining caching behavior (5min-1h). This adds meaningful context about data provenance, privacy, and freshness beyond the structured annotations.

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 compact and efficient: a clear hook, explicit outputs, a numbered list of use cases, and a final sentence on data source, privacy, and caching. Every sentence earns its place, and the structure makes it easy to scan.

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?

With only one optional parameter, no output schema, and comprehensive annotations, the description covers core behavior, use cases, data provenance, privacy, and freshness. The agent receives enough context to decide when and how to invoke the tool without needing additional details.

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 enum values and a description of the trade-off between short and long windows. The main description only repeats '24h, 7d, or 30d' without adding extra semantic meaning, so the schema carries the weight, and 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 opens with a clear question ('What other AI agents are calling on Pipeworx right now') and then specifies the returned content (top tools, top packs, total call volume) over selectable windows. This clearly distinguishes it from sibling tools like discover_tools or ask_pipeworx by focusing on aggregated agent call 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 'Useful for' list provides three concrete scenarios (discovering hot data sources, confirming a canonical tool, aligning with agent demand), giving clear context on when to use it. However, it does not explicitly mention when not to use it or name alternative 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.