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

The description adds meaningful behavioral context beyond the annotations (readOnly, idempotent, openWorld, non-destructive). It discloses caching behavior ('Cached 5min-1h depending on window'), data privacy ('no PII'), and provenance ('derived from CF analytics-engine'), which are not captured by the annotations. This is a strong addition, though it stops short of detailing rate limits or explicit response format.

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 dense yet efficient. Each sentence serves a purpose: the opening establishes the tool's nature, the second defines outputs, the 'Useful for' list provides practical contexts, and the final sentences cover data provenance and caching. There is no filler, and the most important information is front-loaded. 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?

This is a simple tool with one optional parameter, rich annotations, and no output schema. The description fully covers the tool's purpose, expected return content ('top tools, top packs, and total call volume', 'just (pack, tool, count)'), and important behaviors like caching and privacy. There are no significant gaps for an agent to select and invoke 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?

Schema description coverage is 100%, and the schema already describes the `window` parameter with its enum values and semantics ('24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.'). The main description repeats this information but does not add new parameter-level meaning, so it meets the baseline without exceeding it.

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 states the verb and resource: 'Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d).' This distinctly identifies the tool as a trending/aggregation tool, distinguishing it from siblings like ask_pipeworx or discover_tools.

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 explicitly lists three concrete scenarios: discovering hot data sources, confirming a canonical tool choice, and aligning use cases with common agent needs. This gives strong contextual guidance, though it does not explicitly mention when not to use it or name alternative tools. The window parameter guidance ('Shorter windows surface what's hot right now; longer windows show steady-state demand') also helps choose the right invocation.

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