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

Annotations already declare read-only, open-world, idempotent, non-destructive behavior. The description adds valuable context beyond annotations: self-aggregating signal, derived from CF analytics-engine, no PII, and caching behavior (5min-1h). This fully discloses 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 four sentences, each contributing distinct value: what it does, what it returns, when to use it, and its data/caching characteristics. No redundancy or filler; it is front-loaded with the core purpose.

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 has one optional parameter, no output schema, and rich annotations. The description covers return content (top tools, packs, volume), data source, privacy, and caching, which is fully sufficient for a simple, well-annotated read-only 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?

Schema coverage is 100% and the parameter description in the schema already explains the window enum and the trade-off between shorter and longer windows. The tool description repeats the same information without adding new semantics, so it meets the baseline expectation.

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 reports what other AI agents are calling on Pipeworx, returning top tools, packs, and call volume. It distinguishes itself from siblings like discover_tools by focusing on real-time aggregated usage, not generic 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?

Provides explicit use cases ('Useful for...') with three concrete scenarios, which is clear context for when to use the tool. It does not explicitly name alternatives or exclusions, but the listed use cases are sufficient guidance for most situations.

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