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

Annotations already declare readOnly/openWorld/idempotent, and the description adds useful context like drawing from a live catalog and the format of returned examples. No negative behaviors or side effects need disclosure beyond what annotations 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 dense but each sentence adds value: entry-point definition, return format, call patterns, and usage directive. The opening list of example queries is slightly redundant but serves as useful matching snippets for an agent.

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 one optional parameter, complete annotations, and clear description of return value (categories + exact tool/arg shape), the description fully covers what an agent needs to invoke this tool correctly. No output schema is needed because return behavior is explicitly described.

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 covers the topic parameter with enum-like options, and the description adds extra examples ('finance', 'pharma', 'betting') and clarifies behavior when omitted vs provided (full spread vs focus). This adds meaning beyond the schema's list.

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 function: returning category-bucketed example questions with exact tool/argument assistance. It positions itself as the onboarding entry point and distinguishes from siblings by recommending use before other meta-tools.

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?

Provides explicit 'Use this FIRST' guidance and context for when the agent doesn't know what Pipeworx can do. It mentions learning to call meta-tools as an alternative, but does not give when-not-to-use or compare directly with discover_tools.

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