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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds meaningful context beyond that: it returns dynamically generated examples from a live catalog of thousands of tools, and it can focus by topic. This helps the agent understand the open-world, catalog-driven nature of the response without contradicting the 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 front-loaded with user query phrasings, then explains the return type and parameter usage. It is longer than strictly necessary due to the list of query variants, but each part serves to disambiguate user intent and convey the tool's scope. It is structured enough to read as a cohesive onboarding guide.

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 there is no output schema, the description sufficiently explains what the tool returns: category-bucketed example questions with exact tool and argument shape. It also covers the only parameter and provides strong context about its place in the tool ecosystem. The description is complete for an onboarding tool with simple schema and rich annotations.

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 input schema provides 100% coverage for the single optional 'topic' parameter, including exact allowed values and default behavior. The description echoes these examples ('finance', 'pharma', 'betting') and adds the concept of a cross-category spread, but this is largely redundant with the schema. Since schema coverage is high, the baseline of 3 is appropriate.

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 exact tool and argument shape. It uses specific verbs like 'Returns' and explicitly distinguishes itself from sibling tools by positioning it as the 'FIRST' step and referencing meta-tools like ask_pipeworx and entity_profile.

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 states when to use: '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 and with-topic usage patterns. It does not explicitly name alternative tools or when not to use, but the guidance is clear and context-rich.

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