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

Annotations already declare the tool as read-only, open-world, idempotent, and non-destructive, so the safety profile is covered. The description adds behavioral context by detailing the return format (category-bucketed example questions with exact tool+argument shape) and the effect of passing a topic (focuses the spread), which goes beyond the annotations.

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 dense but well-organized, front-loading natural language queries to aid intent recognition and then clearly explaining the tool's output and usage. Every sentence provides useful information, though the initial list of example queries is somewhat redundant with the overall purpose and could have been shortened.

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?

Given the tool's simple input (one optional parameter) and no output schema, the description fully explains what the tool returns (category-bucketed examples), how to invoke it, and when to use it within the broader context of sibling tools. It covers both the 'what' and the 'how', making it comprehensively documented.

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 covers 100% of parameter documentation with a detailed description of the `topic` parameter, including allowed values and omission behavior. The description adds minimal extra meaning beyond the schema by giving example topics ('finance', 'pharma', 'betting'), but these are already enumerated in the schema, so the description's contribution is marginal.

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 identifies the tool as the onboarding entry point for Pipeworx, stating it returns category-bucketed example questions with exact tool+argument shapes. It uses a specific verb ('returns') and resource ('example questions'), and distinguishes itself from siblings by positioning it as the first tool to use when unsure what to ask.

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 context for when to use the tool ('Use this FIRST when you do not yet know what Pipeworx can do for you') and how to call it (no arguments for full spread, or pass `topic` to focus). It also mentions learning how to call meta-tools, giving clear usage guidance, though it does not explicitly exclude sibling tools like discover_tools.

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