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

Annotations already declare the tool read-only and non-destructive. The description adds meaningful context about the output (category-bucketed examples, live catalog) but does not discuss pagination or rate limits. Since annotations cover the safety profile, the description's extra detail is valuable and sufficient.

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 longer than average but every sentence adds useful information, from natural language triggers to output structure and usage timing. It is front-loaded with example queries and remains focused without fluff.

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 schema (1 optional param) and strong annotations, the description covers what it does, what it returns, when to use it, and how to parameterize it. Even without an output schema, it clearly specifies the return concept (category-bucketed questions with tool+argument shape).

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 only describes 'topic' as an optional string with an enum-like list, but the description adds that omitting it yields a cross-category spread and gives concrete examples ('finance', 'pharma', 'betting'). This clarifies the parameter's effect beyond what the schema provides.

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 discovering what questions to ask, returning category-bucketed example questions with the exact tool and argument shape. This distinguishes it from the sibling 'discover_tools' by focusing on question suggestions rather than tool 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?

The description explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you' and explains how to call it with or without the 'topic' parameter. It also references meta-tools as things to learn to call, providing clear timing and context.

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