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

Beyond the annotations (readOnly, idempotent, non-destructive), the description adds valuable behavioral context: it is 'self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count)' and 'Cached 5min-1h depending on window.' This discloses data provenance, privacy, and freshness characteristics that the annotations do not cover.

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 front-loaded with the core return value, then organizes use cases in a numbered list, and finishes with essential data-quality notes. Every sentence carries useful information and there is no redundant filler. The em-dash aside about source and PII is compact but substantive.

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 tool with one optional parameter and no output schema, the description covers return contents, caching behavior, data privacy, and use cases. It is fully self-contained and gives an agent enough context to decide when and how to invoke it, even without additional structured information.

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 already documents the `window` parameter with valid enum values and a description. The tool description adds further nuance: 'Shorter windows surface what's hot right now; longer windows show steady-state demand,' which enriches the meaning beyond the schema. Given 100% schema coverage, the baseline is 3, but the added semantic context justifies a 4.

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 purpose: 'What other AI agents are calling on Pipeworx right now' and explicitly states the returns: 'top tools, top packs, and total call volume.' This distinguishes it from sibling tools like `discover_tools` by focusing on aggregated usage trends rather than individual discovery or searches.

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 ('Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice... (3) seeing whether your use case aligns...'). This gives clear context for when to use the tool, though it does not explicitly list alternatives or when-not-to-use scenarios.

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