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

Annotations already declare readOnly, openWorld, and idempotent hints, so the safety profile is established. The description adds valuable behavioral context by disclosing that results are 'drawn from the live catalog of thousands of tools' and that each example includes 'the exact tool + argument shape that answers it.' This goes beyond the annotations to set expectations about output richness and dynamic nature.

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, information-dense paragraph. It front-loads with concrete example questions to ground the tool's purpose, and every clause contributes value (returns, tools, topics, usage). However, it reads as a long run-on sentence with em-dashes, which could be better structured with bullet points or shorter sentences for easier parsing. Still, it is appropriately sized for the tool's scope.

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 is complete. It covers what the tool returns, how to invoke it (no args for full spread, or with `topic`), and when to use it first. It also mentions the types of categories (company financials, drugs, etc.) and that results include tool+argument shape, leaving no critical gaps for an agent to select and call 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 input schema already provides 100% coverage of the single `topic` parameter, describing it as an 'Optional focus area' with allowed values. The description repeats these value examples and says it 'focuse[s]' the results, which aligns with the schema but does not add new semantic meaning beyond what is already documented. Thus, baseline 3 is appropriate.

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 with a specific verb ('returns') and resource ('category-bucketed example questions'), and it is explicitly positioned as 'the onboarding entry point' that helps users discover what to ask. It distinguishes itself from sibling tools by noting it teaches how to call meta-tools like ask_pipeworx, entity_profile, and compare_entities.

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 provides explicit usage guidance: '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 explains the optional `topic` parameter to focus the results, and implies that for direct queries users should use the actual tools after learning about them. This makes the when-to-use and how-to-use instructions clear.

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