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

Annotations already declare readOnly/openWorld/idempotent. The description adds valuable behavioral context: it returns category-bucketed example questions drawn from a live catalog of thousands of tools, and that each example includes the exact tool+argument shape. It also explains the effect of omitting `topic` (full spread). No contradictions with annotations.

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 appropriately sized for an onboarding tool, starting with example user questions, then the value proposition, return format, parameter usage, and explicit usage guidance. Every sentence contributes new information without redundancy or fluff.

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?

Despite no output schema, the description thoroughly explains what the tool returns (category-bucketed example questions with tool+argument shapes), covers both invocation modes, and positions it within the sibling tool family by naming the meta-tools it teaches. It is complete for a read-only suggestion tool.

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 already documents `topic` with a list of allowed values, so baseline is 3. The description adds meaningful behavior beyond the schema: passing `topic` focuses the results, while omitting it returns a cross-category spread, and it gives concrete examples ('finance', 'pharma', 'betting'). This enriches parameter understanding.

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 the onboarding entry point for answering 'what can I ask Pipeworx?', returning category-bucketed example questions with the exact tool and argument shape. This distinguishes it from siblings like discover_tools by positioning it as the first tool to use.

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?

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools' and gives concrete examples of when to pass `topic` versus call with no arguments. It names specific meta-tools (ask_pipeworx, entity_profile, compare_entities), providing clear context.

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