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

Annotations already mark this as read-only, idempotent, and non-destructive. The description adds valuable behavior beyond that: it discloses the aggregation source (CF analytics-engine), explicitly states no PII is included, reveals the result structure (pack, tool, count), and provides cache freshness (5min-1h depending on window). This is rich operational context that helps an agent understand side effects and data recency.

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?

Four sentences, each earning its place: the core premise, output details, use cases, and data provenance. Front-loaded with the key statement, no fluff or repetition. The structure makes it easy to scan and understand quickly.

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 tool with one optional parameter and no output schema, the description is thorough. It explains what is returned, the data source, privacy characteristics, and caching behavior. It also covers the key use cases. Nothing essential is missing.

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 coverage is 100% for the single optional 'window' parameter, with the schema already explaining the enum values and tradeoffs (short vs long windows). The description mentions the window choices but adds no new semantic information beyond what the schema already provides, so baseline 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 statement ('What other AI agents are calling on Pipeworx right now') and details the exact outputs (top tools, top packs, total call volume). This distinguishes it from sibling tools like discover_tools or ask_pipeworx by focusing on aggregate usage trends 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 three explicit 'Useful for' scenarios (discovering hot data sources, confirming canonical tool choice, aligning with agent needs), giving concrete guidance on when to use it. It lacks explicit exclusionary statements or named alternatives, but the context is clear enough to guide invocation.

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