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

Annotations already declare readOnlyHint=true and idempotentHint=true, making the tool's safety clear. The description adds value by detailing the output structure (category-bucketed examples, tool+argument shapes) and the effect of the topic parameter, going beyond what annotations provide.

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 well-structured with a front-loaded list of example queries, followed by a concise explanation of output and usage. While it is somewhat lengthy, every sentence serves a purpose and it remains readable.

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 only one optional parameter, no output schema, and a large sibling set, the description is complete. It explains the tool's role as an onboarding entry point, when to use it, what output to expect, and even lists all category options. No gaps remain.

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 covers the single parameter fully with description. The description reinforces the meaning by listing example values (finance, pharma, etc.) and explaining that omitting it gives a full spread. This adds practical context beyond the schema baseline.

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 that this tool returns example questions categorized by topic, with each example linked to the exact tool and argument shape. It distinguishes itself from siblings like ask_pipeworx by explicitly calling it the 'onboarding entry point' and referencing 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 explicitly says to use this tool 'FIRST when you do not yet know what Pipeworx can do' and mentions how to call meta-tools. However, it does not explicitly state when not to use it or compare to alternatives like discover_tools.

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