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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive traits. The description adds behavioral specifics: it returns example questions, can be filtered by topic, and is drawn from a live catalog. It does not contradict annotations and provides useful additional context.

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 lengthy but well-structured: front-loaded with purpose and examples, followed by details on output and usage. Every sentence adds value, though a slight reduction could improve conciseness.

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?

Given no output schema, the description fully explains what the tool returns (category-bucketed example questions with tool+argument shapes). It covers usage context, parameter options, and the tool's role in the workflow, making it complete for an onboarding 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?

Schema coverage is 100% with one optional parameter. The description enriches the meaning by listing valid topic values (e.g., 'finance', 'pharma') and explaining that omitting the parameter yields a cross-category spread, which goes beyond the schema description.

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?

Description clearly identifies the tool as the onboarding entry point for new users, listing example queries and stating that it returns category-bucketed example questions with exact tool and argument shapes. It distinguishes itself from siblings like 'discover_tools' by being the first tool to use when unsure of Pipeworx capabilities.

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 'Use this FIRST when you do not yet know what Pipeworx can do for you' and provides example user intents (e.g., 'what can I ask Pipeworx?'). While it does not detail when not to use, the positive guidance is strong and clear.

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