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

Beyond annotations (read-only, open, idempotent, non-destructive), the description adds valuable context: it is self-aggregating, derived from CF analytics-engine, contains no PII, returns only (pack, tool, count), and is cached 5min-1h depending on window. This gives a clear picture of freshness and data provenance.

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 and uses a scannable 'Useful for' list, then adds worthwhile behavioral details. It is somewhat longer than strictly necessary, but no sentence is wasted.

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 read-only stats tool with one optional parameter, the description covers the return values (top tools, packs, call volume), the window choices, the underlying data source, caching behavior, and privacy characteristics. With no output schema, this is sufficient to set expectations.

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 single 'window' parameter is fully documented in the schema with an enum and explanatory text ('Shorter windows surface what's hot right now; longer windows show steady-state demand'). The tool description repeats this idea without adding new parameter-level meaning, so it stays at the baseline for full schema coverage.

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 question ('What other AI agents are calling on Pipeworx right now') and states the exact outputs: top tools, top packs, and total call volume over a selectable window. This distinguishes it from sibling tools like ask_pipeworx or discover_tools by focusing on aggregated 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 lists three concrete scenarios (spotting current-event data sources, confirming canonical tools before asking a question, checking alignment with agent needs), which is strong when-to-use guidance. However, it does not explicitly mention when not to use it or name alternative tools.

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