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

Beyond the readOnly/idempotent/destructive annotations, the description adds meaningful context: data is self-aggregating from the CF analytics-engine, contains no PII, is just (pack, tool, count), and is cached 5min–1h depending on window. This helps set expectations about freshness and privacy.

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 well-organized and front-loaded with the core purpose, followed by a structured bullet list. It is slightly longer than strictly necessary, but each sentence contributes useful information.

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 fully covers what it returns, why it is useful, caching behavior, and data privacy. The annotations handle safety traits, so there are no significant 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 input schema already provides 100% coverage for the single 'window' parameter, including its enum and explanatory text. The description merely echoes the options and adds a note about caching variability, which does not materially expand 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?

The description clearly states the tool reports what other AI agents are calling on Pipeworx, returning top tools, packs, and call volume over a selectable window. It distinguishes itself from siblings like discover_tools by focusing on social proof and current agent behavior rather than generic discovery.

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?

A 'Useful for' section lists three concrete scenarios, making it clear when to apply the tool. However, it does not explicitly reference alternatives or provide when-not-to-use guidance, so it stops short of a 5.

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