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

Annotations already declare read-only, non-destructive, open-world, idempotent. The description supplements with concrete behavioral specifics: returns category-bucketed example questions, each paired with the exact tool and argument shape, drawn from the live catalog. This explains the internal response structure and adds substantial context beyond the annotation hints.

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, long sentence with multiple examples and clauses, but it remains well-organized and front-loaded with user queries. It packs a lot of useful information without unnecessary repetition, though it could be tighter by splitting into shorter sentences.

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?

With no output schema, the description fully explains what the tool returns (category-bucketed example questions with tool+argument shape) and how to focus results via the topic parameter. Given the low complexity (one optional parameter) and complete annotations, there are no significant informational gaps for an agent to invoke it correctly.

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% and the schema already describes the optional 'topic' parameter with allowed values and the omit-for-full-spread behavior. The description restates this in prose but adds minimal new semantic detail beyond giving examples of topic values (finance, pharma, betting) that are already in the schema's enum-like description. Baseline 3 applies.

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 opens with specific natural-language queries ('What can I ask Pipeworx?') and clearly states its role as the onboarding entry point for new agents. It defines the resource (example questions with tool+argument shape) and distinguishes it from broader discovery tools by emphasizing its focus on generating actionable suggestions.

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 advises 'Use this FIRST when you do not yet know what Pipeworx can do for you' and extends the guidance to learning how to call meta-tools. This provides a clear when-to-use context, effectively positioning it relative to the agent's initial state.

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