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

Annotations already declare this as readOnly and non-destructive, so the safety profile is covered. The description adds meaningful behavioral context by explaining that it returns category-bucketed example questions with exact tool and argument shapes, drawn from a live catalog of thousands of tools, and that it can be called with no arguments or a topic filter. This goes beyond a simple 'returns suggestions' and gives the agent a clearer picture of what to expect.

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 search-like queries to orient the agent, then clearly states the tool's purpose, return content, and usage. It is a bit long (about 90 words) but every sentence contributes value, including the list of categories and the advice to use it first. It could be tightened by removing some thesaurus-like phrases, but it remains well-structured.

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 that there is no output schema, the description fully explains what the tool returns: category-bucketed example questions with the exact tool and argument shape that answers each. It also covers how to call it (no args for full spread, topic to focus) and when to use it (onboarding). The mention of meta-tools adds extra context for integration. No critical gaps are apparent.

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 covers the lone `topic` parameter with a full description of options and behavior (omit for full spread), giving 100% schema coverage. The description adds example values like 'finance', 'pharma', and 'betting' but does not materially improve on the schema's semantic clarity. Since the schema does the heavy lifting, the 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 that this tool is the onboarding entry point for discovering what to ask Pipeworx, returning category-bucketed example questions with exact tool and argument shapes. It uses specific verbs ('Returns', 'Call') and distinguishes itself from siblings by positioning it as the 'FIRST' tool to use when unfamiliar with Pipeworx, which is a unique role among the listed sibling tools.

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 explicitly says to use this tool FIRST when you do not yet know what Pipeworx can do, and provides guidance on how to call it with or without a topic parameter. It also mentions learning how to call meta-tools, which serves as an alternative use case. This is clear when-to-use guidance, although it does not explicitly state when not to use it.

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