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

Annotations already declare read-only, idempotent, and non-destructive behavior, lowering the burden. The description adds valuable context: it is a self-aggregating signal derived from the CF analytics-engine, contains no PII, and is cached 5min-1h depending on window. These details go beyond annotations and help set expectations about data freshness and privacy.

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, front-loaded with the core purpose, followed by actionable use cases and essential behavioral notes. Each sentence earns its place; no filler or redundancy. The structure is easy to scan and understand.

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 everything an agent needs: what it returns, the data structure (pack, tool, count), caching behavior, and use cases. Annotations cover safety profile, so no additional disclosure is required. The description is complete and self-sufficient.

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?

Schema coverage is 100% and the parameter description already explains the 'window' enum and the trade-off between shorter and longer windows. The tool description repeats this information without adding new semantic details. It does mention output fields (pack, tool, count) but that's not directly parameter semantics, so the 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 clearly states the tool's function: showing what other AI agents are calling on Pipeworx, and specifies the outputs (top tools, top packs, total call volume). It distinguishes this from sibling tools like ask_pipeworx (which answers questions) and discover_tools (which likely recommends tools) by focusing on aggregated usage trends. The verb 'Returns' plus explicit resource and window scope make it very specific.

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 provides explicit use cases: discovering hot data sources, confirming canonical tool choice, and checking alignment with agent needs. It also implies when not to use it (for actual questions or deep research), but doesn't name alternative tools or state exclusions, 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.