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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, so the safety profile is clear. The description adds meaningful behavioral context beyond annotations: it notes the data is self-aggregating from 'CF analytics-engine', contains no PII, returns only (pack, tool, count), and is cached 5min–1h depending on window. This enriches the agent's understanding of freshness, privacy, and output format.

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 three sentences long and efficiently front-loaded: it opens with the core value proposition, then provides usage scenarios, then ends with implementation details. Each sentence earns its place. It is slightly longer than strictly necessary, but the added context on use cases and caching justifies the length.

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 all essential aspects: what it returns (top tools, packs, call volume), the data source (CF analytics-engine), privacy (no PII), output shape (pack, tool, count), caching behavior, and practical use cases. There are no significant informational 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 single parameter 'window' has 100% schema description coverage with an enum and a clear explanation of shorter vs longer windows. The tool description only restates the window options without adding new semantics. Since the schema already does the heavy lifting, a baseline of 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 purpose: 'What other AI agents are calling on Pipeworx right now' and specifies it returns top tools, top packs, and total call volume over a recent window. This specific verb+resource+outcome distinguishes it from sibling discovery tools like discover_tools or search_datasets, which focus on finding data sources rather than observing agent usage 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 gives three explicit use cases: discovering hot data sources, confirming a popular tool as canonical, and checking alignment with agent needs. It also explains how window choice affects results. It does not name sibling alternatives or state when not to use the tool, but for a read-only trending lookup, the intended usage is well conveyed.

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