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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral context beyond this: it states the data is self-aggregating from CF analytics-engine, contains no PII, and is cached for 5min-1h. This gives the agent important information about freshness, privacy, and provenance not present in 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 efficiently structured: the first sentence states purpose, the second lists outputs, then three numbered use cases, and a final sentence on data source/caching. Every sentence adds distinct value, with no fluff or repetition, and the most critical information is front-loaded.

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?

Despite having no output schema, the description explains exactly what the agent can expect: 'top tools, top packs, and total call volume' and 'just (pack, tool, count)'. It also covers the window parameter, data provenance, privacy, and caching. For a simple, read-only trending tool, this is complete and leaves no significant gaps.

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 covers 100% of the parameter descriptions, including a detailed explanation of the window enum ('Shorter windows surface what's hot right now; longer windows show steady-state demand'). The tool description merely mentions the window options without adding new semantic detail, so the baseline score of 3 applies per the rubric.

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 statement: 'What other AI agents are calling on Pipeworx right now' and immediately specifies the outputs: top tools, top packs, and total call volume over a recent window. This distinguishes it from sibling tools like ask_pipeworx (question answering) and discover_tools (discovery), with no ambiguity about its function.

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 three explicit use cases with bullet points, such as discovering hot data sources and confirming a canonical tool before asking a question. This gives clear context for when to use it, though it does not explicitly name alternatives or state when not to use it, which prevents a 5.

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