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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable context about the return format (category-bucketed examples with tool+argument shape) and the live catalog source, but does not disclose any additional behavioral traits beyond what annotations cover.

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 moderately long but well-structured, front-loading common queries and explaining purpose, categories, and usage. Every sentence adds value, though it could be slightly more concise.

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's simplicity (1 optional parameter, no output schema), the description fully covers what the agent needs: what it returns, when to use it, and how it relates to sibling tools. It explicitly mentions the return structure and the meta-tools it helps learn.

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% with a description. The description adds explicit allowed values (finance, pharma, etc.) and clarifies usage patterns (no arguments vs topic), providing meaningful guidance beyond the schema alone.

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: to return category-bucketed example questions with exact tool+argument shapes. It distinguishes itself from siblings by positioning as the onboarding entry point and explicitly mentioning meta-tools like ask_pipeworx, entity_profile, etc.

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 guidance on when to use this tool: 'Use this FIRST when you do not yet know what Pipeworx can do for you'. It also explains how to call it (no arguments for full spread, topic parameter to focus) and mentions it helps learn how to call meta-tools, effectively differentiating from alternatives.

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