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

Annotations already declare the tool read-only, open-world, idempotent, and non-destructive. The description adds valuable context beyond those flags, such as the return value structure (category-bucketed example questions with tool + argument shapes), the live catalog source, and the 'no arguments vs. topic' behavior, which together give a clear behavioral model.

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 longer than minimal but front-loads natural language queries and the 'onboarding entry point' context. Every sentence contributes useful information—purpose, return content, argument behavior, and when to use it—so it is structured and purposeful, though slightly verbose for a single optional parameter.

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 fully explains what the tool returns (category-bucketed example questions with exact tool and argument shape), how to invoke it (with/without `topic`), and when to use it first. It also lists a wide range of covered categories, making the tool's scope unambiguous and complete for an onboarding assistant.

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 fully, including allowed values and the omission behavior. The tool description reinforces this with examples like 'finance', 'pharma', 'betting' and adds the 'full spread' nuance, but the marginal added meaning beyond the schema is minimal, so the baseline of 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 identifies the tool as the onboarding entry point for discovering what Pipeworx can do, with specific verbs like 'returns category-bucketed example questions' and lists concrete categories. It distinguishes itself from siblings by framing its role as the 'first' tool to use when unfamiliar with Pipeworx and explicitly references learning to call 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?

The description gives explicit usage context ('Use this FIRST when you do not yet know what Pipeworx can do') and shows how to call it with or without the `topic` argument. It does not provide explicit when-not-to-use guidance or name a direct alternative, but the recommended first-use positioning makes the usage clear enough.

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