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

Annotations already mark this read-only, idempotent, open-world, and non-destructive. Description adds valuable behavioral context: it is self-aggregating, sourced from CF analytics-engine, excludes PII, contains only (pack, tool, count), and is cached 5min-1h by window. This far exceeds the annotation baseline.

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 tightly packed sentences front-load the core purpose, then the colon-list of use cases and a final data-privacy note. Every sentence adds distinct value with no filler, making it easy to scan.

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?

With one optional parameter, rich annotations, and a description that states the exact returned shape (top tools, top packs, total call volume, (pack, tool, count)), the agent has sufficient information to invoke and interpret results. No output schema exists but return semantics are already described, and caching behavior covers freshness 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?

Input schema covers 100% of the single optional parameter with enum values and a description of hot vs steady-state windows, so baseline is 3. Tool description only reinforces the same window choices and adds a vague cache duration dependency, providing little additional parameter meaning beyond the schema.

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 opens with 'What other AI agents are calling on Pipeworx right now' and states exact outputs (top tools, top packs, total call volume). This clearly distinguishes from sibling discovery tools by focusing on aggregated cross-agent usage trends rather than individual recommendations. Specific verb 'Returns' plus resource scope merits 5.

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?

Lists three concrete use cases: finding hot data sources, confirming canonical tool choices, and checking alignment with other agents' needs. This gives clear context for when to call, but does not explicitly name alternatives or exclusion criteria, so 4 rather than 5.

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