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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful behavioral detail beyond this: it explains the return structure ('category-bucketed example questions'), the source ('drawn from the live catalog of thousands of tools'), and modality ('Call with no arguments for the full spread, or pass topic to focus'). This gives the agent a clear picture of what will happen when invoked.

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 somewhat long, but it is front-loaded with high-value information: the natural-language trigger examples, the output summary, and the usage directive. Every sentence contributes necessary context for a discovery tool. Minor redundancy exists (e.g., 'what can I ask' is repeated in multiple forms), but structural clarity is strong.

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 tool with only one optional parameter, no output schema, and a low complexity profile, the description is complete. It explains what the tool returns, when to use it, how to call it (with and without arguments), and how it fits into the broader tool ecosystem. No significant 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?

The schema covers 100% of parameters, but the description adds value beyond it by explaining the no-argument invocation ('Call with no arguments for the full spread') and providing concrete examples of valid topic values ('finance', 'pharma', 'betting'). It also clarifies the meaning of omission ('Omit for a cross-category spread'), which is not present in 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 uses a specific verb and resource: it is 'the onboarding entry point' that 'returns category-bucketed example questions' with 'the exact tool + argument shape.' It clearly distinguishes itself from sibling tools by emphasizing its role as the first-stop discovery mechanism and by explicitly naming the meta-tools it helps the agent learn to call.

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 says to 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).' This gives clear context on when to use it. It does not explicitly state when not to use alternatives like discover_tools, but the guidance is specific enough for effective selection.

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