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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is a safe, non-mutating query. The description adds valuable detail about the output structure: it returns category-bucketed example questions with exactly how to call other tools. There is no contradiction 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 moderately long but well-structured. It begins with example user queries, then defines the output, the parameter usage, and finally the recommended use case. Every sentence adds essential information, though a minor trim could improve flow. It earns a 4 for efficiency and clarity.

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 the tool's simplicity (one optional parameter, no output schema), the description thoroughly covers what the tool returns, how to use it, and when to use it. It explains the nine categories and the fact that results are drawn from a live catalog of thousands of tools. No additional information is needed for effective use.

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 single optional parameter 'topic', with a description of allowed values. The description adds context by explaining that omitting the parameter gives a full cross-category spread, while passing a topic focuses on that category. It also lists example topic values, reinforcing 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 starts with concrete user intents ('What can I ask Pipeworx?', 'give me ideas') and immediately positions the tool as 'the onboarding entry point'. It specifies the unique output: 'category-bucketed example questions ... each with the exact tool + argument shape'. This clearly distinguishes it from sibling tools like 'discover_tools' or 'ask_pipeworx'.

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 states when to use: 'Use this FIRST when you do not yet know what Pipeworx can do for you'. It also explains how to narrow results with the optional 'topic' parameter. While it doesn't list explicit alternatives, the context implies that after using this tool, users should move to specific tools like 'ask_pipeworx' or 'entity_profile'.

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