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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the bar is lower. The description adds meaningful behavioral context beyond that: the response is category-bucketed example questions, each paired with the exact tool and argument shape, drawn from the live catalog. It also discloses the behavior of omitting vs. providing the topic parameter, which is useful and not redundant with the 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 dense, single paragraph that is front-loaded with natural user questions. Every clause contributes either return behavior, category examples, or usage guidance; there is no filler or repetition. It is slightly long, but the length is justified by the onboarding purpose and the need to convey the tool's value and calling conventions.

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, so the description must explain return values, and it does: it clearly states the response contains category-bucketed example questions, each with the exact tool and argument shape, drawn from the live catalog. It also covers when to use it, how to call it with no arguments or a topic, and how it relates to meta-tools. This is complete for a discoverability tool with simple parameters and strong annotations.

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?

Schema coverage is 100% since the single optional topic parameter is fully described with its allowed values and the note to omit for a cross-category spread. The description reinforces this with examples like 'finance', 'pharma', and 'betting', and reiterates the no-argument behavior, but it adds no new semantic information beyond what the schema already provides. Baseline 3 is appropriate.

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 states a specific verb+resource with clear scope: it returns category-bucketed example questions showing which Pipeworx tools answer what, and explicitly positions itself as 'the onboarding entry point.' It distinguishes itself from sibling tools like ask_pipeworx (actual querying) and discover_tools (tool discovery) by focusing on what to ask and how to learn tool-calling patterns.

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?

It gives explicit usage 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 names specific alternatives (ask_pipeworx, entity_profile, compare_entities) and describes both the no-argument full-spread call and the topic-focused variant. This is clear when-to-use direction that goes beyond generic statements.

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