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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. Description adds caching details (5min-1h), data source (CF analytics-engine), and privacy assurance (no PII). 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?

Two well-structured paragraphs. First sentence front-loads purpose. Every sentence adds value: return values, use cases, caching, and derivation details. No redundant or vague language.

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 simple read-only trend tool with one optional parameter and no output schema, the description is fully complete. It covers what is returned, how data is aggregated, caching, privacy, and use cases. No gaps.

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 covers 100% of parameters. Description adds semantic meaning to the window parameter by explaining that shorter windows surface hot trends and longer windows show steady-state demand, beyond the enum list.

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 recent window'. It uses a specific verb ('returns') and resource ('trending data'), and distinguishes 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?

Lists three explicit use cases and explains window choice (short window for hot trends, long window for steady-state). Lacks explicit exclusions or when-not-to-use guidance, but context is clear enough.

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