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

The annotations already include readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, and the description adds substantial behavioral context beyond those: account and plan requirements, parallel tool routing, findings-packet return shape with gaps[] and contradictions[], latency expectations, and the guarantee that citations are fetchable. These details clearly exceed the annotation coverage.

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 long and dense, but it is front-loaded with the account requirement and the primary alternative, and every sentence adds relevant information about behavior, limitations, or selection criteria. It could be improved with paragraph breaks or bullet structure, but no significant fluff exists.

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 and a complex tool, the description fully covers what the agent needs to invoke it correctly: input requirements, return format (findings packet with evidence/confidence/source/fetched_at/citation, gaps[], contradictions[], hop field), expected latency, and auth prerequisites. It is especially strong in explaining edge cases like empty gaps[] and the guarantee against invented findings.

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 description coverage is 100%, so the baseline is 3, but the description adds meaningful usage context beyond the schema by explaining what each depth level does in practice ('quick=3 (single hop)', gap-recovery hops, contradictions scan) and that 'thorough' requires a paid plan. This extra behavioral meaning helps an agent choose the right depth value more effectively than the schema alone.

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 states a specific action and resource: 'Grounded multi-source research across Pipeworx's 1540 STRUCTURED data sources in ONE call' and explicitly distinguishes itself from open-web search and the ask_pipeworx siblings. It also names its best-use case ('broad/multi-part questions over structured data'), so an agent can clearly differentiate it from the other research 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?

The description provides explicit when-to-use and when-not-to-use guidance: use ask_pipeworx when not signed in, for a single lookup, and for breaking or current-news topics. It also explains which depth level is appropriate for gap recovery and iterative follow-up, leaving no ambiguity about selecting this tool over its siblings.

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