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

Annotations already declare readOnly/idempotent/openWorld and non-destructive. The description adds valuable behavioral context beyond that: it returns category-bucketed example questions, includes exact tool + argument shapes, and can either return a full spread or focused results based on topic. This enriches the agent's understanding of the output and invocation semantics.

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 fairly long but every sentence contributes: it opens with natural language queries, defines the purpose, describes the return payload, and gives usage guidance. It is front-loaded with what the agent would ask, and the length is justified by the onboarding role. No fluff or repetition exists.

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 effectively complements the structured data by explaining what the tool returns (category-bucketed example questions with tool+argument shapes). It covers invocation modes (no args vs. topic) and the primary use case, making the tool fully understandable for an agent. The 1 optional parameter and annotations are simple, so the description provides complete context.

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 covers the single optional parameter 'topic' with description, giving 100% coverage. The description adds extra meaning by listing example topic values ('finance', 'pharma', 'betting') and explaining the effect of omitting vs. providing the topic. This goes beyond the schema's bare enumeration, earning a score above baseline.

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 is the onboarding entry point for a newly connected agent to discover what Pipeworx can do. It specifies the verb (suggest/return example questions) and resource (category-bucketed examples from the live tool catalog), while distinguishing it from siblings like discover_tools by emphasizing the exact tool + argument shape returned.

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?

Explicit usage guidance is provided: 'Use this FIRST when you do not yet know what Pipeworx can do for you'. It also explains when to call with no arguments vs. passing a topic, and references meta-tools as alternatives. This clearly differentiates from sibling tools and gives actionable when-to-use.

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