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

The description adds valuable behavioral context beyond the annotations: it explains the data source ('derived from CF analytics-engine'), privacy ('no PII'), output structure ('just (pack, tool, count)'), and caching ('Cached 5min-1h depending on window'). These details help the agent understand freshness and scope. No contradiction with annotations exists; in fact, the description reinforces the read-only, open-world nature.

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 compact and well-structured: one opening sentence defining the tool, a bulleted list of three use cases, and a final sentence covering data provenance and caching. Every sentence carries distinct information, with no redundancy or filler. The most important 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?

For a simple tool with one parameter and no output schema, the description covers the essential elements: what it returns (top tools, packs, call volume), the data format (pack, tool, count), and the time windows. It could specify whether results are sorted or limited (e.g., 'top N'), but given the self-explanatory nature of trending data, the current level is sufficient for an agent to use it correctly.

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 the same semantics ('24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.'). The description adds no new parameter-level details beyond what the schema already provides. Since schema coverage is 100%, 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 statement: '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 identifies the exact resource (Pipeworx call data), the verb (returns), and the output scope (trending tools/packs/volume). It also distinguishes itself from sibling tools like discover_tools and ask_pipeworx by focusing on aggregated agent behavior rather than individual queries or tool discovery.

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 lists three concrete use cases: discovering hot data sources, confirming a canonical tool, and checking use-case alignment. This gives clear context for when to use the tool. It does not explicitly name alternative tools or state when not to use it, but the use cases provide sufficient guidance for a simple read-only analytics tool. The window-selector guidance also helps choose between 24h/7d/30d.

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