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

Beyond the readOnly/idempotent annotations, the description adds substantial behavioral context: self-aggregating signal, derived from CF analytics-engine, no PII, cached 5min-1h depending on window, and the exact data shape (pack, tool, count). 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 front-loaded with the core purpose, followed by a well-organized bullet list of use cases, and ends with technical caveats (caching, data source, PII). Every sentence provides value and the structure makes it easy to scan.

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?

For a simple read-only tool with one optional parameter, the description covers return format, caching behavior, privacy guarantees, and usage scenarios. Even without an output schema, the agent understands what data will come back.

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 provides 100% coverage of the single 'window' parameter, including enum values and their trade-offs ('Shorter windows surface what's hot right now; longer windows show steady-state demand'). The description merely restates the window options without adding new meaning, so baseline 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 specific, distinctive statement ('What other AI agents are calling on Pipeworx right now') and clearly enumerates the outputs: top tools, top packs, and total call volume. This clearly distinguishes it from sibling tools like discover_tools and ask_pipeworx.

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, checking alignment with agent needs), and the window choice is explained. However, it does not explicitly name alternatives or state when NOT to use this tool, 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.