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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description adds valuable behavioral detail: it returns category-bucketed example questions, each with the exact tool and argument shape, drawn from a live catalog of thousands of tools. It also explains the optional topic focus behavior. No contradiction 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 somewhat long due to the initial list of example user phrasings, but this list is useful for intent matching. The core purpose, behavior, and usage are clearly stated in structured sentences that follow the front-loaded examples. Every part serves a purpose, though it could be trimmed slightly.

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?

Despite having no output schema, the description fully explains what the tool returns: category-bucketed example questions with tool and argument shape. It also covers the parameters, use cases, and relationship to meta-tools. The annotations handle safety context, making the description complete for an agent to invoke the tool confidently.

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 describes the single optional 'topic' parameter with a list of values and meaning, providing 100% coverage. The description adds further context by giving examples ('finance', 'pharma', 'betting') and clarifying that omitting the parameter yields a cross-category spread. This reinforces and slightly extends 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 identifies the tool as an onboarding entry point that returns category-bucketed example questions with the exact tool and argument shape for each. It uses a specific verb ('suggest') and resource ('Pipeworx capabilities'), and distinguishes from siblings by positioning itself as the 'first' step for learning what to ask and how to call 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?

It gives explicit guidance to '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 also explains the no-argument vs. topic-argument options. However, it does not explicitly state when not to use it or name alternative tools like discover_tools, though it references meta-tools as related.

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