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

The description adds substantial behavioral context beyond the read-only/idempotent annotations: it reveals caching (5min-1h), data provenance (CF analytics-engine), no-PII guarantee, and that the response is a simple (pack, tool, count) tuple. No contradiction 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 compact and well-organized: a direct one-sentence summary of the tool's output, followed by a bulleted list of use cases, and a final technical note on data source, privacy, and caching. Every sentence contributes value without 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?

For a simple read-only aggregator with one optional parameter and no output schema, the description covers the purpose, use cases, return content, caching behavior, and data privacy. It is fully self-sufficient for an agent to decide when to call 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?

The input schema already has 100% coverage, including a clear description of the window parameter ('Shorter windows surface what's hot right now; longer windows show steady-state demand'). The tool description repeats the window options but adds no new parameter-specific meaning, so the schema-driven 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 starts with a clear, specific statement: 'What other AI agents are calling on Pipeworx right now.' It then details the exact outputs (top tools, top packs, total call volume) and the time windows, distinguishing it from sibling tools like discover_tools by focusing on aggregated real-time call trends rather than general discovery.

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 gives three concrete scenarios: discovering hot data sources, confirming canonical tools, and checking alignment with other agents. This provides clear context for when to use the tool, though it does not explicitly name alternatives or state when not to use it.

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