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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=true. The description adds transparency about the return content (category-bucketed examples with exact tool+argument shape) and that it draws from a live catalog. This is useful context beyond the annotations, though the annotations already cover safety.

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 paragraph, front-loaded with example queries. It is fairly long but every sentence adds value. Minor verbosity for a simple tool, but not excessive.

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 tool with one optional parameter and no output schema, the description covers what it does, when to use, and what to expect. It is fully adequate for an agent to select and invoke this tool correctly.

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 parameter (topic). The description enriches by listing valid focus areas (finance, pharma, etc.) and clarifies that omitting the argument gives a full cross-category spread. This adds practical value beyond the schema description.

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 example questions bucketed by category. It specifies the verb ('returns') and the resource ('category-bucketed example questions'), and distinguishes it from siblings by positioning it as the first tool to use when learning about Pipeworx.

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 provides guidance on optional argument usage ('Call with no arguments for the full spread, or pass `topic` to focus'). While it doesn't explicitly list when not to use, the context is strong.

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