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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive. The description adds valuable context beyond these: it returns category-bucketed examples, each with the exact tool and argument shape, drawn from the live catalog. No contradictions.

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 long but densely informative. It front-loads the key purpose and then packs categories, usage modes, and context into a flowing paragraph. Every sentence contributes; no filler, but it could be tightened slightly for readability.

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 simple optional-parameter tool with no output schema, the description fully covers what the tool does, what it returns, how to invoke it, and when to use it. There are no remaining gaps for an agent to guess.

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% for the single 'topic' parameter. The description goes further by listing example values ('finance', 'pharma', 'betting') and explaining the default behavior (omit for cross-category). This adds practical meaning beyond the schema.

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 opens with a clear verb+resource: 'suggest questions' for Pipeworx. It explicitly distinguishes itself from sibling tools like ask_pipeworx by positioning itself as the onboarding entry point that returns example questions with tool call shapes, not answers. This is specific and 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?

Provides explicit when-to-use: 'Use this FIRST when you do not yet know what Pipeworx can do for you.' It also gives actionable guidance on calling with no arguments vs. passing a topic. It stops short of naming alternatives like ask_pipeworx directly, but the context strongly implies the distinction.

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