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

Annotations already establish this as a safe, read-only, idempotent operation. The description adds valuable behavioral context: it is 'self-aggregating' from CF analytics-engine, strips PII, returns only (pack, tool, count), and is 'cached 5min-1h' depending on window—clarifying data freshness and privacy beyond the annotation set.

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 compact (about 80 words) and front-loaded with the core function. Every sentence serves a purpose: the first defines the tool, the next lists use cases, and the last adds provenance/caching. No wasted words or redundant restatement of the tool name.

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, this description is remarkably complete. It covers what it returns, the meaning of the window parameter, ideal use cases, data source, privacy (no PII), and caching behavior. An agent can confidently decide when and how to call it without further clarification.

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' is fully documented in the schema with enum values and a clear explanation of trade-offs. The description merely restates the window options without adding further meaning. Since schema coverage is 100%, the description doesn't need to compensate, and it correctly leaves parameter behavior to 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?

The description opens with a specific, unique value proposition—'What other AI agents are calling on Pipeworx right now'—and clearly states it returns top tools, packs, and call volume. This distinguishes it from siblings like discover_tools and ask_pipeworx by focusing on aggregated community call patterns.

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?

Three explicit, practical use cases are listed (spotting hot data sources, confirming canonical tools, aligning with agent needs). While it doesn't directly name alternative tools to use instead, the scenarios imply when this is the right choice, and the cache/window guidance helps with selection. Just missing an explicit 'compare with X to get...' caveat.

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