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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description goes beyond this by adding that the signal is self-aggregating from CF analytics-engine, contains no PII, and is cached for 5 minutes to 1 hour depending on window. These details meaningfully set expectations about data freshness and privacy without contradicting 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 three tight sentences: the first explains what it returns, the second lists concrete use cases, and the third covers data source/caching. No filler or repetition. The structure is front-loaded with the most important information and remains easy to scan.

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 read-only aggregation tool with one optional parameter and no output schema, the description covers return fields, window semantics, use cases, data provenance, privacy, and caching. This is sufficient for an agent to decide when to call it and what to expect, without needing additional detail.

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 the single 'window' parameter has a clear enum. The description adds interpretive value by explaining that shorter windows surface what's hot right now while longer windows show steady-state demand. This enriches the raw schema without being redundant.

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 for what other AI agents are calling on Pipeworx. It names the specific resource ('Pipeworx trending') and adds window options, making it distinct from siblings like discover_tools. The verb 'Returns' is direct and the scope is immediately understood.

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?

Provides concrete use cases: discovering hot data sources, confirming canonical tools, and checking alignment with agent needs. While it doesn't explicitly name alternative tools or say when not to use it, the 'Useful for' list gives clear situational guidance. The mention of 'self-aggregating signal' helps set expectations about what kind of insight it provides.

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