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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable context: it returns example questions with exact tool+argument shape drawn from a live catalog, and explains that calling with no arguments gives the full spread while a topic narrows it. This goes beyond what annotations provide, though it doesn't cover potential rate limits or output format details.

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 average but every sentence earns its place: it starts with natural language query examples, explains the output structure and source, covers the parameter usage, and gives explicit placement guidance. It is front-loaded and well-structured, only losing a point for length that could be trimmed slightly.

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 no output schema, the description compensates by explaining exactly what is returned (category-bucketed example questions with tool+argument shapes). With one optional parameter, full schema coverage, and strong annotations, the description provides sufficient context for an agent to select, invoke, and interpret the tool correctly. No gaps remain.

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?

Schema coverage is 100% and the schema already documents the optional topic parameter with allowed values. The description reinforces this by giving examples ('finance', 'pharma', 'betting') and clarifies the behavioral effect of omitting versus passing the parameter ('full spread' vs 'focus'), adding semantic value beyond the schema's dry list of enums.

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 is an onboarding entry point that returns category-bucketed example questions for what to ask Pipeworx. It names a specific verb+resource ('returns category-bucketed example questions') and distinguishes it from siblings like ask_pipeworx by framing it as a meta-tool for learning how to call other 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?

Explicit usage guidance is provided: '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 how to call with or without the topic parameter, giving concrete examples of focus areas and alternatives (meta-tools) to learn about.

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