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

The annotations already mark this as readOnly, openWorld, idempotent, and non-destructive, and the description adds meaningful context beyond that: it is self-aggregating, derived from CF analytics-engine, contains no PII, and is cached for 5 minutes to 1 hour depending on the window. This gives the agent a strong behavioral model of what it is querying.

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 well-structured, front-loading the core purpose and then using a numbered list for use cases and a short final sentence for technical caveats. It contains minor redundancy around 'hot/current' language, but every sentence earns its place and the structure aids skimmability.

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, a fully descriptive schema, and strong annotations, the description covers what an agent needs to invoke the tool correctly: what it returns, the available windows, the caching behavior, and the privacy/aggregation properties. Since there is no output schema, the description's mention of 'top tools, top packs, and total call volume' provides sufficient return-value context.

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 input schema already provides 100% coverage for the single 'window' parameter, including the enum values, the default, and guidance on short vs long windows. The tool description only restates the window options and does not add unique parameter semantics beyond what the schema already covers, 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 opens with a specific, question-shaped statement — 'What other AI agents are calling on Pipeworx right now' — then names the concrete outputs: top tools, top packs, and total call volume. This clearly positions it as a meta/trending tool, distinct from related siblings like discover_tools, without needing to open the schema.

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 gives three explicit, practical use cases: discovering hot data sources, confirming a canonical tool, and checking alignment with broader agent demand. It does not state when not to use this tool or explicitly name an alternative, so it falls just short of the top bar, but the usage context is clear and actionable.

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