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

Beyond annotations (readOnly, idempotent, non-destructive), the description adds valuable context: caching behavior ('Cached 5min-1h depending on window'), data provenance ('derived from CF analytics-engine'), and privacy ('no PII, just (pack, tool, count)'). These details disclose how the data is gathered and its freshness.

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 well-structured: a hook, output summary, numbered use cases, and data provenance/caching notes. Every sentence contributes value, and the numbered list makes the use cases easy to scan. No redundant or fluff content.

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 tool with one optional parameter and no output schema, the description covers the output contents, use cases, data source, privacy, and caching. It is fully self-contained and leaves no critical gaps for an agent to resolve.

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 full parameter coverage with an enum and semantic explanation ('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 semantics beyond what the schema already states.

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 question 'What other AI agents are calling on Pipeworx right now' and enumerates exact outputs: top tools, top packs, and total call volume over a recent window. This clearly distinguishes it from siblings like discover_tools and ask_pipeworx, which serve different purposes.

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 description explicitly lists three concrete use cases (discovering hot data sources, confirming a canonical tool, checking alignment with user needs). It does not mention alternatives or when not to use, but the use cases are specific enough to guide appropriate selection.

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