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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds context on return format (category-bucketed examples with tool shapes) and parameter behavior (full spread vs focused). No contradictions. Lacks details like rate limits or performance, but still adds value beyond 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 single paragraph but front-loaded with key question types. It efficiently packs information without fluff, though slightly longer than necessary. Still well-structured and clear.

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?

For a tool with one optional parameter and no output schema, the description covers purpose, usage scenarios, parameter details, and return format comprehensively. No missing critical information given the tool's simplicity.

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% for the single parameter 'topic'. The description adds practical guidance: lists example values ('finance', 'pharma', etc.) and explains that omitting the parameter gives a cross-category spread, which is not explicit in the schema.

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 it is an onboarding entry point that returns category-bucketed example questions with tool+argument shapes. It uses specific verb 'suggest' and resource 'questions', and distinguishes from sibling tools by indicating it should be used first when the agent does not know Pipeworx capabilities.

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?

Explicitly states when to use ('first when you do not yet know what Pipeworx can do') and provides examples of queries that trigger it. It also mentions the optional topic parameter to focus the results and implies alternatives (meta-tools like ask_pipeworx, entity_profile).

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