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

Annotations already declare the operation read-only, idempotent, open-world, and non-destructive. The description adds valuable behavioral context: it explains the output structure (category-bucketed examples, each with tool and argument shape), that results come from a 'live catalog of thousands of tools,' and how the topic parameter affects the response. This exceeds what annotations alone 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 a single, information-dense paragraph that front-loads the purpose with example queries and later covers output, parameter behavior, and usage timing. It is arguably slightly longer than needed, but every sentence contributes meaningful context and the structure is logical.

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 on its own. It does so comprehensively: it lists the exact categories returned, states the format (tool + argument shape), and explains the optional parameter's effect. Combined with the strong annotations, this makes the tool's behavior fully predictable.

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 topic field has a description listing allowed values. The description supplements this with concrete examples ('finance', 'pharma', 'betting') and clarifies that omitting the parameter returns a cross-category spread, adding practical guidance beyond 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 the tool's purpose: it 'Returns category-bucketed example questions ... each with the exact tool + argument shape that answers it.' It is an onboarding entry point, distinct from siblings like ask_pipeworx (which answers questions) and discover_tools (which lists tools). The opening example queries reinforce the function.

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 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).' This provides a clear when-to-use context, but it does not explicitly state when not to use it, so it stops short of a perfect score.

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