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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses that the tool returns 'category-bucketed example questions' with 'the exact tool + argument shape' drawn from a live catalog. It also explains behavior when called with no arguments versus with a topic, and mentions the focus areas. This provides rich behavioral context not captured in 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 long (~200 words) but front-loaded with example questions and structured with clear sections. Every sentence contributes useful information, including when to use, what to expect, and how to call. It could be slightly more concise, but the length is justified by the explanatory value.

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?

The tool is simple (0 required parameters, no output schema), so the description fully covers its behavior: what it returns, how to call it, and when to use it. It explains the output format (category-bucketed questions with tool + argument shape) without needing an output schema. No significant gaps remain.

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 already covers the `topic` parameter with its possible values, so the baseline is 3. The description adds value by explaining the default behavior when omitted ('full spread') and provides concrete examples of topic values ('finance', 'pharma', 'betting'). This goes beyond simply restating 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 as 'the onboarding entry point for an agent that just connected and wants to know what is worth asking.' It specifies the verb (suggest questions) and resource (Pipeworx capabilities), and distinguishes it from sibling tools like ask_pipeworx and discover_tools by explicitly naming them as meta-tools to learn about. The example queries make the scope concrete.

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 gives explicit usage direction: '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 how to invoke with or without the `topic` parameter, and lists example topics. This clearly tells the agent when to choose this tool over alternatives.

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