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

Annotations already mark read-only/open/idempotent; description adds meaningful behavioral context: self-aggregating signal, derived from CF analytics-engine, no PII, only (pack, tool, count), and cache TTL. This helps the agent understand freshness and privacy implications.

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?

Three well-structured sentences with a scannable 'Useful for' list. Every sentence contributes—purpose, use cases, and behavioral traits. No filler.

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 single-optional-param tool with no output schema, the description fully covers what's returned, how it's derived, privacy, and caching. It also positions the tool among siblings. Very complete for its 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 has 100% coverage with enum and explanatory text describing window trade-offs. The tool description merely references the window values without adding additional meaning, so baseline 3 applies.

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 opening line 'What other AI agents are calling on Pipeworx right now' instantly conveys purpose; then it states it returns top tools, top packs, and call volume over a window. This clearly distinguishes from siblings like discover_tools or recall, which list/find individual resources rather than aggregate trending signal.

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 three explicit 'Useful for' scenarios covering discovery, validation, and alignment. However, it does not explicitly name alternatives or when NOT to use it, so falls short of the highest benchmark.

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