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

Annotations already declare the tool as read-only, open-world, idempotent, and non-destructive, so the baseline is lower. The description adds useful behavioral context: it returns category-bucketed example questions drawn from a live catalog, and explains the dynamic nature of results. This goes beyond annotations by describing the output structure and data source.

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 relatively long but each sentence adds value: it lists example queries, explains the return format, covers the parameter, and gives usage guidance. The structure is front-loaded with natural language prompts, making it easy to parse. Slightly overlong but still efficient for the amount of information conveyed.

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 has no output schema, but the description clearly explains what is returned (category-bucketed example questions with tool+argument shapes). It also covers the parameter, use cases, and relationship to meta-tools. For an onboarding tool, this is complete and self-sufficient, providing all necessary context for an agent to invoke it 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?

The schema provides full coverage of the single optional parameter, so the baseline is 3. The description adds meaning by explaining that omitting topic gives a full spread, while passing a topic focuses the results, and it gives concrete examples of valid values (finance, pharma, betting). This enhances the schema's basic 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 states the tool's purpose: it returns category-bucketed example questions to help users understand what Pipeworx can do. It specifies the resource (Pipeworx capabilities), the verb (returns example questions), and the scope (with exact tool + argument shapes). This differentiates it from siblings like discover_tools or ask_pipeworx by framing it as the onboarding entry point.

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 to use this FIRST when you don't know what Pipeworx can do, and provides guidance on calling without arguments vs with a specific topic. It also mentions that it can teach how to call meta-tools, which is helpful context. It does not explicitly exclude cases when it should not be used, but the 'FIRST' guidance implies a clear use case.

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