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

Despite annotations already declaring readOnly, idempotent, and non-destructive behavior, the description adds valuable context: it is self-aggregating, derived from CF analytics-engine, contains no PII, and has a caching policy (5min-1h depending on window). This goes beyond annotations and helps the agent understand the data source and freshness.

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 well-structured and front-loaded with the core purpose. It uses a numbered list for use cases and compact clauses for extra details (data source, PII absence, caching). Every sentence adds value without redundancy, making it efficient despite its length.

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 tool with one optional parameter and no output schema, the description covers all relevant aspects: what it returns, available time windows, when to use it, data provenance, and caching behavior. There are no significant gaps given the tool's 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?

The input schema already provides 100% coverage for the single 'window' parameter, including enum values and a description explaining the trade-offs between shorter and longer windows. The main description mentions the window option but does not add new semantic detail beyond the schema, so a baseline score of 3 is appropriate.

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 purpose ('What other AI agents are calling on Pipeworx right now') and lists exact return values (top tools, top packs, total call volume). This distinguishes it from siblings like discover_tools or ask_pipeworx by focusing on aggregate trending data rather than discovery or Q&A.

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 description provides an explicit 'Useful for' list with three concrete use cases (discovering hot data sources, confirming canonical tool choice, aligning with agent needs). This gives clear context for when to use the tool, though it does not explicitly state when not to use it or name alternative tools.

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