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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context beyond these: 'Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.' This discloses data source, privacy traits, and caching behavior, which enriches the agent's understanding of what to expect.

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 somewhat longer than minimal but well structured: a lead sentence, a return-value sentence, a numbered use-case list, and a final technical note. Every sentence contributes meaning without redundancy. It is not as terse as the 5 exemplar, but it earns its length with substantive 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?

With no output schema, the description still conveys the core return content ('top tools, top packs, and total call volume') and important behavioral details (no PII, derivation from analytics, cache times). For a simple one-parameter tool with robust annotations, this is largely complete. It might have elaborated on exact response format, but that is not critical for selection and invocation.

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% for the single optional window parameter, with a clear enum and description in the schema itself. The description mentions the same window values ('24h, 7d, or 30d') but adds no additional semantic detail beyond the schema. Baseline 3 is appropriate since the schema carries the parameter meaning.

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. Returns the top tools, top packs, and total call volume over a recent window.' This identifies the verb (returns) and resource (trending Pipeworx usage). It clearly distinguishes this from siblings like discover_tools or list_recent by focusing on aggregate agent call 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?

The description provides explicit use cases: 'Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need.' However, it does not mention when not to use this tool or name specific alternatives (e.g., discover_tools), so it lacks the explicit exclusion that would warrant a 5.

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