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

Annotations already provide readOnlyHint, idempotentHint, and openWorldHint. The description adds behavioral details: 'self-aggregating signal', 'derived from CF analytics-engine', 'no PII', and caching behavior ('5min-1h'). No contradictions.

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?

Three clear sentences plus a bullet list of use cases. Front-loaded with an engaging question: 'What other AI agents are calling on Pipeworx right now?' No redundant 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?

The description covers return values (top tools, packs, volume), data source, privacy, caching, and use cases. For a simple read-only tool with 1 optional param, this is complete. Minor gap: doesn't mention what 'top' means (e.g., by call count) but is 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?

Schema coverage is 100% and both schema and description explain the 'window' parameter. The description adds value by explaining the tradeoff between short and long windows ('hot' vs 'steady-state demand'), enriching the schema's static enum.

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, with a specific verb ('returns') and resource ('Pipeworx trending'). It distinguishes from siblings like 'discover_tools' by focusing on aggregate usage trends.

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 concrete use cases (discovering hot data sources, confirming canonical choice, checking alignment) and contrasts window sizes ('hot' vs 'steady-state'). However, it does not explicitly state when to avoid using this tool or mention alternatives.

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