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

Despite favorable annotations (readOnly, idempotent, non-destructive), the description adds meaningful behavioral context: it states the data is self-aggregating, derived from an analytics engine, contains no PII, and has a cache TTL of 5min-1h. This goes well beyond the annotations and sets accurate expectations for freshness and sensitivity.

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 front-loaded with the core purpose. The 'Useful for' list is efficient, and the final sentence on data provenance/caching earns its place. The only slight deduction is for the mildly jargon-heavy phrase 'Self-aggregating signal,' but it is immediately clarified.

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?

The tool is simplicity itself: one optional parameter, no output schema. The description covers the return values (top tools, packs, call volume), the window options, the use cases, and the data source/caching behavior. There are no significant gaps for an agent to get lost.

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?

With 100% schema description coverage, the baseline is 3. The description enhances this by elaborating on the window parameter: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This adds decision-useful semantics beyond the schema's simple enum.

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, vivid statement ('What other AI agents are calling on Pipeworx right now') and explicitly lists the outputs: top tools, top packs, and total call volume. This clearly differentiates it from sibling tools like discover_tools, which likely focuses on discovery rather than 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?

The 'Useful for' section enumerates three concrete scenarios, providing clear context for when to invoke this tool. It also explains window semantics to guide parameter choice. However, it does not explicitly mention when not to use it or name alternative tools, 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.