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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, and the description adds substantial behavioral context beyond that. It specifies the return format (category-bucketed example questions with tool + argument shape), the categories covered, the effect of the topic parameter, and that it draws from the live catalog of thousands of tools. 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 dense but effective; it packs the purpose, output, parameter behavior, and usage guidance into one long paragraph. Every sentence contributes, but the structure could be improved with bullet points or shorter sentences. It is appropriately sized for the information it conveys.

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 and how to invoke it. It covers the main use case, the topic parameter, and the source of the suggestions, making it complete for an agent to decide when and how to call it. The lack of mention of pagination or rate limits is not a gap for this onboarding tool.

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 describes the single `topic` parameter with a list of possible values, but the description adds meaningful guidance: examples ('finance', 'pharma', 'betting'), instruction to omit for a cross-category spread, and the note that topic focuses the output. This goes well beyond the schema's basic coverage.

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 for an agent to discover what questions are worth asking, returning category-bucketed example questions with the exact tool and argument shape. It distinguishes itself from siblings by explicitly saying 'Use this FIRST' and naming the meta-tools it teaches you to call (ask_pipeworx, entity_profile, compare_entities). The verb 'suggest' and resource 'questions' are specific.

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 when-to-use guidance: 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also explains the optional topic filtering and the default behavior when called with no arguments, giving the agent clear context for choosing this tool versus alternatives.

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