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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses the actual behavior: it returns categorized example questions drawn from the live catalog, each paired with the exact tool and argument shape. This gives the agent a concrete expectation of the output and how the tool behaves. No contradiction with 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 a single dense paragraph but well-structured: it opens with natural language queries, states the core function, lists categories, explains the return value, and gives usage instructions. Every sentence contributes useful information, though slightly verbose with the full category list and examples.

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 has one optional parameter and no output schema, the description covers everything needed: purpose, usage, output content, and when to use it. It even names the meta-tools the agent can learn about. The description is complete and self-sufficient.

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 describes the `topic` parameter with examples, and the description adds meaning by explaining how to use it ('pass topic to focus') and providing more examples ('finance', 'pharma', 'betting'). This goes beyond the schema's static list, so the description adds value.

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's purpose: it is the onboarding entry point that returns category-bucketed example questions with the exact tool and argument shape to answer them. The verb 'suggest_questions' and the title 'What Can I Ask Pipeworx?' align perfectly, and it explicitly distinguishes itself from sibling tools by saying 'Use this FIRST when you do not yet know what Pipeworx can do for you.'

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?

Usage guidance is explicit: it instructs the agent to use this tool FIRST when unfamiliar with Pipeworx or to learn how to call meta-tools. It also provides conditional usage: 'Call with no arguments for the full spread, or pass topic to focus.' This clearly defines when to use the tool and how to tailor the call.

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