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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context beyond these: it is a 'self-aggregating signal — derived from CF analytics-engine, no PII', and 'cached 5min-1h depending on window'. This explains the data provenance, privacy characteristics, and freshness behavior, which are not covered by 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 concise and well-structured: a one-sentence purpose, a 'Useful for' list with three bullet points, and a closing note on aggregation and caching. Each sentence serves a distinct function, no filler, and the most essential information is front-loaded.

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?

There is no output schema, so the description partially compensates by stating it returns 'top tools, top packs, and total call volume' and clarifying the data shape as 'just (pack, tool, count)'. It also notes caching behavior. For a simple one-parameter tool, this is fairly complete, though the exact return format (e.g., field names, structure) is not fully specified.

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?

The only parameter ('window') is fully documented in the schema with an enum and description explaining the trade-off between shorter and longer windows. The description merely repeats the window values without adding nuance. Schema coverage is 100%, so the description adds little for parameter semantics, meeting the baseline of 3.

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 opens with a clear, specific statement of what the tool does: 'What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window.' This clearly identifies the verb (returns), the resource (trending call data), and distinguishes it from siblings like discover_tools or recent by focusing on aggregated AI-agent call patterns.

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 'Useful for' list provides three explicit use cases: discovering hot data sources, confirming a canonical tool, and aligning use cases with agent needs. This offers clear context for when to use the tool. It does not explicitly name alternatives, so it misses the 'when-not/alternatives' element, but the context is strong.

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