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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds context beyond that: it explains that the tool draws from a live catalog of thousands of tools, returns example questions with tool+argument shapes, and provides category details. This extra context is useful and doesn't contradict any annotation.

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 fairly long but front-loaded with example phrases that immediately indicate the tool's purpose. Every sentence provides distinct value: examples, output content, parameter behavior, and strategic usage guidance. Minor verbosity in the middle but no wasted space.

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 tool's complexity as an onboarding meta-tool, the description is complete. It explains what the tool returns (category-bucketed example questions with tool+argument shapes), how to call it (no args vs topic), and its relationship to other meta-tools. With no output schema, this description adequately covers expected return values and usage context.

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 describes the `topic` parameter fully, but the description adds valuable semantics: it lists example topics (finance, pharma, betting), explains the parameter is optional, and clarifies that omitting it yields a cross-category spread. This goes well beyond the schema's enum-like list and helps the agent decide how to invoke the tool.

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 is the onboarding entry point for learning what Pipeworx can do, returning category-bucketed example questions with exact tool and argument shapes. This precise verb+resource+scope distinguishes it from sibling tools like ask_pipeworx or deep_research, positioning it as the 'FIRST' exploratory step.

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 when to use the tool: '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 focus using the optional `topic` parameter and when to omit it, providing clear context for both use and non-use.

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