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

The description explains that the tool returns category-bucketed example questions with exact tool+argument shapes, drawn from a live catalog. It also details the return structure (company financials, drugs, etc.). Annotations already mark the tool as readOnly, openWorld, idempotent, and non-destructive, and the description is fully consistent with these traits, adding valuable behavioral context beyond the 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 a single, long sentence that packs a lot of information, including common queries, return format, and usage guidance. While every part is useful, it could be slightly more concise by removing some of the redundant examples at the beginning. However, it is well-structured and front-loaded with the most important information.

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?

Given the complexity of an onboarding tool with no output schema, the description covers everything needed: purpose, usage scenario, return structure (category-bucketed examples with tool arguments), parameter behavior, and even mentions related meta-tools. It is complete and leaves no critical gaps.

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 input schema already describes the `topic` parameter with possible values. The description adds the behavior: omitting the parameter yields a cross-category spread, while providing a topic focuses the results. Since schema coverage is high (100%), the description provides meaningful extra context about the effect of using or not using the parameter.

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 Pipeworx, listing natural language queries that trigger it and explicitly distinguishing it as the first tool to use when exploring capabilities. It specifies the verb 'returns category-bucketed example questions' and the resource 'the live catalog of thousands of tools', making the purpose unambiguous.

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 '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 provides concrete examples of when to use the tool (e.g., 'what can I ask?', 'getting started') and implies it is not needed once the agent is familiar with the available tools. This is clear usage guidance with proper context.

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