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

Annotations already indicate readOnlyHint, idempotentHint, destructiveHint, so the agent knows it's safe. The description adds valuable behavioral context: data source (CF analytics-engine), no PII, cache duration (5min-1h), and that it's self-aggregating. No contradictions with annotations.

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 with no wasted words. It front-loads the main output, then lists uses, then adds technical details. Every sentence earns its place, and the structure is logical.

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 1 optional parameter and no output schema, the description fully explains inputs (window), outputs (top tools, packs, volume), source, caching, and privacy. Nothing missing given the tool's complexity.

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 includes a description for the window enum. The description adds semantic context: 'Shorter windows surface what's hot right now; longer windows show steady-state demand', which helps the agent choose the appropriate window beyond the schema's literal description.

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 configurable window, with specific verb 'returns' and resource 'trending data on Pipeworx'. It distinguishes from siblings by emphasizing aggregation of other AI agents' calls, which is unique among sibling tools like discover_tools or entity_profile.

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 use cases (discovering hot data, confirming canonical choice, seeing alignment) and explains trade-offs between short and long windows. However, it does not mention when not to use or directly contrast with siblings like discover_tools, which could be a related alternative.

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