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

Annotations already mark it read-only/idempotent/open-world. The description adds valuable behavior: data derived from CF analytics-engine, no PII, aggregation structure (pack, tool, count), and caching freshness (5min-1h depending on window). This exceeds the minimum and gives agents appropriate expectations.

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?

Well-structured: a one-sentence summary, three concrete use cases, and a compact technical note. No filler or redundancy; each clause adds information.

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?

For a single-optional-param read-only trend query, the description covers purpose, use cases, data source, privacy, caching, and return contents. No output schema exists, so the return description ('top tools, top packs, total call volume') is sufficient.

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?

There is one optional parameter (window) with a complete schema description and enum. The tool description mentions the window options but does not add new semantics beyond schema. The schema itself explains short vs long window trade-offs, so a baseline 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 its function: showing what other AI agents are calling on Pipeworx and returning top tools/packs/call volume over a window. This differentiates it from sibling tools like discover_tools or recent_alerts by focusing on aggregate agent 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?

Provides explicit useful-for scenarios (discovering hot data sources, confirming canonical tool, checking alignment) but does not name alternative tools or state when not to use it. Clear context and intended use cases warrant a 4.

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