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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral context: that it returns example questions with exact tool and argument shapes, drawn from a live catalog, and that calling with no arguments gives a 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 longer than average but every sentence provides necessary context. It is front-loaded with common user questions, then details the output and parameter. No irrelevant content given the tool's onboarding purpose.

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 tool with one optional parameter and no output schema, the description covers all relevant aspects: purpose, when to use, parameter guidance, and output content. Annotations are comprehensive, so no 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?

Schema coverage is 100%, but the description adds value by enumerating the specific topic values ('finance', 'pharma', etc.) that are not in the schema (no enums). It also explains the behavior when the parameter is omitted versus provided, which is not 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 clearly specifies the tool's purpose as an onboarding entry point that returns category-bucketed example questions. It uses a specific verb ('returns') and resource ('example questions'), and distinguishes from siblings like ask_pipeworx and discover_tools by noting it's for 'getting started' and for agents that 'do not yet know what Pipeworx can do'.

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 states when to use: '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 provides guidance on the `topic` parameter to focus the results, implying when not to use (if topic is known) and giving alternatives via the tool catalog.

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