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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds valuable behavioral context beyond these: data provenance ('derived from CF analytics-engine'), privacy guarantee ('no PII'), output shape ('just (pack, tool, count)'), and caching behavior ('Cached 5min-1h depending on window'). No contradictions with 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 three sentences with zero waste: the first sentence states purpose and output, the second lists use cases, the third gives data provenance, privacy, and caching. It is front-loaded with the main purpose and every sentence earns its place.

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?

Given the tool's low complexity (one optional parameter, no output schema, no nested objects), the description provides adequate context: what is returned, the data source, privacy implications, and caching behavior. It is sufficient for an agent to decide whether to invoke the tool and what to expect from the response, though it does not precisely define the output schema.

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%: the only parameter 'window' is fully described with an enum and detailed semantics ('24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.'). The description references the window in prose but adds no additional parameter-level meaning, so the baseline of 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 description opens with a specific question ('What other AI agents are calling on Pipeworx right now') and immediately states what it returns: 'top tools, top packs, and total call volume' over a recent window (24h, 7d, or 30d). This clearly distinguishes it from sibling tools like discover_tools or recent_alerts, which serve different purposes.

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' section lists three explicit, concrete scenarios for when to use this tool: discovering hot data sources, confirming canonical tool choices, and checking alignment with other agents' needs. It provides clear context but does not explicitly state when not to use it or name alternative tools, so it stops short of a 5.

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