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

Description adds significant context beyond annotations: source (CF analytics-engine), privacy (no PII), data structure (pack, tool, count), and caching policy (5min-1h). Annotations already indicate readOnly, openWorld, idempotent, non-destructive, so the description enriches understanding of behavior.

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?

Three sentences with clear structure: first sentence defines output, second lists use cases, third covers derivation and caching. No redundant or missing information. Excellent conciseness.

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 and no output schema, the description covers return values (top tools, packs, call volume) and data format (pack, tool, count). Lacks explicit format or example but is sufficient for agent understanding. Caching and derivation add completeness.

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?

Only one parameter (window) with full schema description (100% coverage). The description reinforces the window options and their implications (hot vs. steady-state) but adds little beyond what the schema already provides. 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?

Description clearly states it returns top tools, top packs, and total call volume over a window. Lists three specific use cases (discovering hot data sources, confirming canonical tool, aligning use case). Distinguished from sibling tools like discover_tools and ask_pipeworx 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?

Provides explicit scenarios for use (current events, canonical tool confirmation, alignment check). Does not explicitly mention when not to use or alternatives, but the context is clear enough for an AI agent to infer proper usage relative to siblings.

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