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

Annotations already provide readOnly, openWorld, idempotent, non-destructive hints. Description adds caching behavior (5min-1h), data source (CF analytics-engine), and privacy note (no PII). This is valuable beyond annotations but does not contradict them.

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?

Description is succinct and front-loaded: first sentence states the main action, followed by a numbered list of use cases and additional context. Every sentence serves a purpose with no redundancy.

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?

Given the tool's simplicity (one optional parameter, no output schema), the description covers purpose, parameters, use cases, caching, and data source adequately. It lacks explicit return format details but that's acceptable without an output schema.

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 has 100% coverage with one enum parameter. Description adds semantic context about window selection (shorter for hot, longer for steady-state). This slightly extends the schema's description, which already had basic enum labels.

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?

Clearly states the tool returns top tools, top packs, and total call volume over a window of 24h, 7d, or 30d. The verb 'Returns' and resource 'Pipeworx trending data' are specific, and it distinguishes from siblings by focusing on what other AI agents are calling, which is a unique niche among many discovery tools.

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: discovering hot data sources, confirming canonical tools, and checking alignment. Provides guidance on window selection. However, it does not mention when not to use the tool or directly compare to alternatives like discover_tools or suggest_questions.

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