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

Annotations already establish readOnlyHint, openWorldHint, and idempotentHint, so the bar for added context is moderate. The description adds valuable behavior disclosure: data is cached for 5min-1h depending on window, and the signal derives from CF analytics-engine with no PII. This gives agents insight into data freshness and privacy but omits specific rate limits or auth requirements, which are not critical given the safety 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 tightly written: three sentences covering purpose, use cases, and technical details without redundancy. It is front-loaded with the main claim and efficiently captures all necessary information.

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 tool with one optional parameter and no output schema, the description is sufficiently complete. It explains the return contents (top tools, packs, call volume), the windows, the caching behavior, and the privacy guarantee. An agent can confidently select and invoke this tool with minimal missing 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 fully documents the single `window` parameter with enum values and descriptions. The tool description adds semantic nuance beyond the schema by noting that shorter windows surface what's hot right now while longer windows show steady-state demand, aiding selection.

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 function: it returns top tools, top packs, and total call volume for what other AI agents are calling on Pipeworx. It specifies the resource (Pipeworx trending data) and the action (returns), making it immediately distinguishable from siblings like discover_tools or ask_pipeworx.

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 provides explicit use cases: discovering hot data sources, confirming canonical tool choice, and checking alignment with current usage. While it doesn't name alternative tools explicitly, the use cases imply when this tool is appropriate and when other tools (like discover_tools) might be better suited.

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