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

Even though annotations already mark this as read-only, open-world, idempotent, and non-destructive, the description adds meaningful behavioral context: it is self-aggregating, derived from CF analytics-engine, contains no PII, returns only (pack, tool, count), and is cached 5min-1h depending on window. This goes well beyond what the annotations alone convey.

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 tight and well-structured: it leads with the core signal, then states the return values, then gives practical use cases, then adds important data provenance and caching details. No sentence is wasted, and the information is front-loaded.

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 simple one-optional-parameter tool with no output schema, this description is complete: it explains what is returned, the available windows, the underlying data source, privacy characteristics, caching behavior, and practical applications. An agent has everything needed to decide whether and how to call it.

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% for the single optional window parameter, so the schema already explains the enum and the trade-off between shorter and longer windows. The description does not add much parameter-level detail beyond restating the window options and cache behavior, 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 clearly states the tool returns top tools, top packs, and total call volume over a window, which is a specific verb-resource pairing. It conveys the trending/social-proof nature of the data, making it easy to distinguish from a general tool-discovery tool like discover_tools.

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 gives concrete contexts: surfacing hot data sources, validating canonical tool choice, and checking alignment with other agents' needs. It provides clear usage guidance without explicitly naming alternatives or when-not-to-use cases.

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