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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds valuable context beyond annotations: 'derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.' This discloses data derivation, privacy, and caching behavior, which is meaningful for an AI agent.

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 and well-structured. It opens with the core purpose, then provides a numbered list of use cases, followed by implementation details. Every sentence earns its place; there is no fluff or repetition.

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?

Despite lacking an output schema, the description explains what the return contains ('top tools, top packs, and total call volume') and specifies the exact data shape ('just (pack, tool, count)'). Combined with the simple single-parameter interface and rich annotations, this is behaviorally complete for 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% with a descriptive enum for the 'window' parameter. The description adds semantic nuance beyond the schema by explaining trade-offs: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This enriches parameter understanding and helps agents choose appropriately.

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: 'What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d).' This uses a specific verb ('Returns') and distinct resource (trending tool usage), differentiating it from siblings like discover_tools or ai_visibility_check.

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 for when to use this tool: discovering hot data sources, confirming canonical choices, and assessing alignment with agent needs. It also contrasts window choices ('Shorter windows surface what's hot right now; longer windows show steady-state demand'). However, it does not explicitly exclude alternatives or name when-not-to-use, so it lacks the full 'when-not' guidance seen in top-tier examples.

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