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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, providing a safe, read-only profile. The description adds behavioral details: returns category-bucketed examples, all from the live catalog, and varying output based on the optional topic parameter. No contradictions with annotations. It could have added more about potential response size, but the annotations cover the critical safety aspects.

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 dense but every sentence earns its place. It front-loads with natural language examples, then states the core function, return format, and parameter behavior. It could be split into cleaner sentences, but it is not bloated or repetitive. The structure supports quick scanning by an AI agent.

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?

For a discovery tool with no output schema, the description fully covers what the agent receives (category-bucketed examples with tool+argument shape), how to use the parameter, and when to invoke it. It also explains how it relates to meta-tools like ask_pipeworx. Given the tool's simple scope, this is complete and self-sufficient.

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% for the single optional 'topic' parameter, which already lists valid focus areas. The description adds real value by explaining that omitting the argument gives a full cross-category spread versus passing a topic to focus, and gives concrete examples ('finance', 'pharma', 'betting'). This clarifies the parameter's effect beyond the schema's static list.

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 the onboarding entry point for an agent, with a specific verb (suggest questions) and resource (what to ask Pipeworx). It distinguishes itself from siblings by stating it returns category-bucketed example questions with the exact tool+argument shape, and by framing itself as the 'FIRST' tool to use when unfamiliar with Pipeworx. This is far more specific than a generic 'suggest ideas'.

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.' This provides clear when-to-use guidance. It doesn't explicitly name alternatives like discover_tools, but the context strongly implies this is the initial discovery mechanism. A small deduction for not explicitly stating when NOT to use it.

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