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

Annotations already declare readOnly, openWorld, and idempotent. The description adds meaningful behavioral context: it returns 'category-bucketed example questions' with 'the exact tool + argument shape', drawn from a 'live catalog of thousands of tools'. It also notes that calling with no arguments gives the full spread, and passing `topic` focuses the results. This goes beyond the annotations without contradicting them.

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 a single dense paragraph that packs in many example queries, the tool's purpose, output details, call instructions, and usage guidance. While every sentence carries useful information, the structure is not optimized for scanning—it could benefit from bullet points or clearer separation of 'what' and 'when'. It is informative but slightly verbose.

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?

The tool has one optional parameter and no output schema, yet the description fully covers what the tool does, what its output looks like (category-bucketed examples with tool and argument shapes), and when to use it. It even explains the source ('live catalog') and how to narrow results. Given the tool's simplicity, the description is complete for an agent to decide when and how to invoke it.

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 one parameter, so the baseline is 3. The description adds value by providing concrete examples ('finance', 'pharma', 'betting') and explaining that the parameter 'focuses' the results, which is not fully explicit in the schema description alone. It also clarifies that omitting the parameter yields a cross-category spread.

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 identifies the tool as an onboarding entry point that returns category-bucketed example questions, each with the exact tool and argument shape. It includes the verb 'returns' and the resource ('category-bucketed example questions'), and distinguishes itself from siblings by framing itself as the 'FIRST' step to learn how to use meta-tools like ask_pipeworx and entity_profile.

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 guidance is given: 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' This establishes when to use the tool and references alternatives (meta-tools), effectively telling the agent to start here before using others. It also explains how to call it with or without the `topic` parameter.

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