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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds valuable context: cached 5min-1h, derived from CF analytics-engine, no PII, and output shape (pack, tool, count). This gives agents accurate expectations about freshness, privacy, and return format beyond the 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?

Front-loaded with the core purpose and output, then structured into a compact 'Useful for' list, followed by a concise note on data source and caching. Every sentence contributes value; no filler or redundancy.

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?

With one optional parameter and no output schema, the description compensates fully by specifying what is returned (top tools, top packs, total call volume), the output format (pack, tool, count), the caching behavior, and the absence of PII. An agent would know how to invoke it and what to expect.

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 covers 100% of the single parameter, including enum values and the semantic distinction between shorter windows (hot right now) and longer windows (steady-state demand). The tool description merely restates the window options without adding new 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, substantive statement of what the tool does: '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 verb+resource+scope clearly differentiates it from sibling tools like discover_tools and ask_pipeworx, which focus on discovery or direct Q&A, not aggregate usage trends.

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?

Provides three explicit use cases (discovering hot data sources, confirming a canonical tool before asking, and checking alignment with common agent needs). While it doesn't name specific alternatives or state when not to use, the 'Useful for' list constitutes clear contextual guidance, earning a 4 rather than a 5.

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