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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as safe. The description adds value by detailing the return format (category-bucketed example questions with exact tool+argument shape) and the optional topic parameter's effect. No contradictions 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 front-loaded with example queries and packs essential information into a few sentences. It is slightly verbose but every sentence adds value. No redundancy.

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 is an onboarding entry point with one optional parameter and no output schema, the description is complete. It covers purpose, usage, and return format. It also references related meta-tools for further learning.

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 sole parameter 'topic' has 100% schema coverage. The description enhances understanding by listing example values (finance, pharma, etc.) and clarifying that omitting it gives a cross-category spread. This adds practical meaning beyond the schema definition.

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 as an onboarding entry point for agents wanting to know what to ask Pipeworx. It provides example queries ('What can I ask?', 'give me ideas') and specifies it returns category-bucketed example questions with exact tool+argument shape. It distinguishes from siblings by positioning itself as the first tool to use when learning Pipeworx's capabilities, and by contrasting with 'discover_tools' and other meta-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 provides clear guidance: call with no arguments for full spread, or pass a 'topic' to focus. It explicitly recommends using this tool 'FIRST when you do not yet know what Pipeworx can do for you'. While it doesn't explicitly state when not to use it, the context is sufficient for an AI agent to make a reasonable choice.

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