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

Annotations already declare read-only, idempotent, open-world, and non-destructive behavior. The description adds that results are dynamically drawn from a live catalog of thousands of tools and include exact calling shapes, which is valuable behavioral context beyond what annotations convey.

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 dense, run-on paragraph that front-loads a list of example user queries. While every clause carries useful information, it could be broken into clearer sections and is longer than necessary.

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?

With no output schema, the description responsibly explains what the tool returns (category-bucketed questions with tool+argument shapes) and covers both invocation modes. Combined with full parameter documentation, the tool is completely specified.

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 description already covers the `topic` parameter with a full list of focus areas and the omit behavior. The description's examples add no new semantic information, so it stays at the baseline for high schema coverage.

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 role as an onboarding entry point that returns category-bucketed example questions with the exact tool and argument shape. It uses specific verbs like 'Returns' and distinguishes from sibling tools by positioning this as a first step to learn meta-tools.

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?

It explicitly instructs to '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, and the optional `topic` parameter is explained for both focused and broad use.

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