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

Even though annotations already declare this a safe read-only operation, the description adds substantive behavioral context: data source ('derived from CF analytics-engine'), privacy ('no PII'), output shape ('just (pack, tool, count)'), and caching behavior ('Cached 5min-1h depending on window'). These details meaningfully go beyond the annotations and help the agent anticipate results.

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: the main statement is front-loaded, followed by a bulleted 'Useful for' section and a final note on data provenance. It's slightly long but every sentence contributes value. No fluff or 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?

Given the tool's simplicity (one optional parameter, no output schema, no nested objects), the description is thorough. It explains what the tool returns, the window options, use cases, and even the data source and caching. Since there is no output schema, the description successfully conveys the return shape ('top tools, top packs, total call volume') and the tuple structure.

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 schema already documents the window parameter with its enum and explanatory description ('shorter windows surface what's hot right now; longer windows show steady-state demand'). The tool description repeats this in the opening line but adds no new meaning. Schema coverage is 100%, so the baseline 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.' It then explicitly states the return value ('top tools, top packs, and total call volume') and the time window options. This distinguishes it from siblings like discover_tools (which likely enumerates available tools) and ask_pipeworx (a Q&A tool).

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' list provides three concrete scenarios: discovering hot data sources, confirming canonical tools, and checking alignment with other agents. This gives clear context for when to use the tool. However, it does not explicitly mention alternatives or when not to use it, so it stops short of a full 5.

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