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

Annotations already declare read-only, open-world, and idempotent behavior. The description adds valuable context: self-aggregating signal from CF analytics-engine, no PII, just (pack, tool, count), and caching behavior (5min-1h depending on window). This enriches the annotation without contradicting it.

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 well-structured and front-loaded with the core purpose. The bullet list adds value but is slightly longer than strictly necessary. Every sentence earns its place, but it could be tightened without losing meaning.

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 tool with one optional parameter and no output schema, the description is complete: it explains the return shape (top tools, top packs, call volume), data source, privacy, caching, and use cases. No important context is missing.

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 schema already provides meaningful parameter description (window enum with interpretation). The tool description simply repeats the window values without adding new semantics beyond the schema, 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 clearly states what the tool does: it returns trending tool usage from other AI agents, including top tools, packs, and call volume over a recent window. The verb 'Returns' and specific resource make the purpose unambiguous, distinguishing it from sibling tools 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?

Explicitly lists three concrete use cases for when to use this tool, such as discovering hot data sources and confirming canonical tool choices. It does not mention when not to use it or alternative tool names, so it falls short of a perfect 5.

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