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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds value by revealing the data source (CF analytics), stating no PII is collected, and specifying caching behavior (5min-1h). This extends beyond the annotation hints.

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 concise at three sentences, each earning its place. It front-loads the core purpose, then lists use cases, then adds technical caveats. No wasted words.

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?

Given the simple single-parameter schema and the absence of an output schema, the description adequately covers what the tool returns (top tools, packs, call volume). It does not detail the output structure but the context signals indicate no nested objects, so it's 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?

The sole parameter 'window' is fully described in the schema with enum values and a description. The tool description adds context about the trade-off between short and long windows (hot vs. steady-state). This adds semantic value beyond the schema.

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's purpose: returning top tools, packs, and call volume over time windows. It uses a specific verb-noun structure and distinguishes itself from sibling tools like discover_tools and ask_pipeworx by focusing on trending/aggregate data.

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 explicitly lists three use cases (discovering hot data, confirming canonical tools, aligning use cases). While it doesn't explicitly state when not to use or name alternatives, the context is clear and practical for an AI agent to decide when to invoke this tool.

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