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, so the safety profile is covered. The description adds valuable behavioral context: it returns a dynamic, live catalog-derived list of category-bucketed example questions, each with the exact tool and argument shape. It also clarifies the effect of omitting or passing the topic parameter. 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 information-dense but still readable, starting with common query phrasings to quickly orient the reader. It covers purpose, return format, usage with/without parameters, and when to use it. It is slightly long due to the list of categories, but every sentence contributes useful information without 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?

There is no output schema, but the description thoroughly explains what the tool returns: category-bucketed example questions with exact tool names and argument shapes. It also covers invocation variants (no args vs. topic), the categories available, and the tool's role in onboarding. For a tool of this simplicity, the description is complete and leaves no significant gaps.

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 input schema already provides a description of the 'topic' parameter with focus area options, giving 100% schema coverage. The description enhances this by showing concrete example values (e.g., 'finance', 'pharma', 'betting') and explaining that omitting it returns a cross-category spread, which adds meaning beyond the schema's static list.

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 Pipeworx, with a specific verb ('returns') and resource (category-bucketed example questions). It distinguishes itself from siblings by positioning itself as the 'use FIRST' tool and by referencing meta-tools like ask_pipeworx, entity_profile, and compare_entities, which is particularly useful for navigation.

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 gives explicit when-to-use guidance ('Use this FIRST when you do not yet know what Pipeworx can do for you') and mentions how to learn to call meta-tools. It does not explicitly state when not to use it or name alternatives like discover_tools, but the 'use FIRST' directive strongly implies it is not for direct research. This is clear context without full exclusionary guidance.

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