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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the safety profile is clear. The description adds valuable behavioral context: the output is dynamically drawn from a 'live catalog of thousands of tools', returned in category buckets, and includes tool+argument mappings. It also clarifies the no-argument default behavior. It does not contradict annotations, and the added context exceeds what the annotations alone provide.

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 contributes: it lists example queries, describes the output structure, specifies parameter behavior, and gives explicit usage context. The information is front-loaded with user-facing phrasings, making the purpose immediately clear. It is dense but not wasteful, earning a 4 rather than a 5 for slight length.

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 there is no output schema, the description's explanation of the return format (category-bucketed example questions with exact tool+argument shapes) is essential and well covered. It also addresses the only parameter, the default behavior, and even names related meta-tools. The description fully equips an agent to decide when and how to invoke this tool, making it contextually complete.

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 description for 'topic' lists allowed values, but the tool description enriches it with concrete examples ('finance', 'pharma', 'betting') and explains that omitting it yields a cross-category spread. This adds meaning beyond the schema's straightforward enumeration, justifying a score above the baseline.

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 states the tool's purpose: it is the onboarding entry point that returns category-bucketed example questions with the exact tool and argument shape needed to answer them. This distinguishes it from sibling tools like ask_pipeworx (which directly answers questions) and discover_tools (which likely lists capabilities). The verb 'Returns' plus the specific resource ('example questions') makes it unambiguous.

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 provided: '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 names alternatives (ask_pipeworx, entity_profile, compare_entities) and gives parameter usage instructions (omit for full spread, pass topic to focus). This fully addresses when and how to use it.

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