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

Annotations already mark the tool as read-only, open-world, idempotent, and non-destructive, and the description does not contradict them. It adds substantial behavioral context beyond the annotations: account and paid-plan requirements, parallel decomposition across 5,798 tools, explicit gap reporting with no invented findings, contradictions[] behavior, citation resolvability, semantic excerpting, and expected latency. This is rich, accurate transparency.

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?

Although longer than typical descriptions, every sentence earns its place: auth and alternative routing are front-loaded, followed by core behavior, return format, depth semantics, and latency expectations. The structure flows logically from prerequisites to function to output details. Nothing feels redundant or decorative.

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?

For a complex research tool with no output schema, the description thoroughly covers what an agent needs: when to use it, auth constraints, fallback alternatives, depth tradeoffs, response contents, citation behavior, gap handling, and runtime expectations. It is complete enough that an agent can select and invoke the tool correctly without additional documentation.

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?

Input schema coverage is 100%, so the parameters are already well documented. The description adds meaningful context beyond the schema by clarifying the paid-plan requirement for depth:'thorough' and by reinforcing that question supports broad natural-language decomposition. It does not need to restate schema details, and it adds enough extra semantics to justify 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 opens with a clear, specific function: grounded multi-source research across Pipeworx's 1,517 structured data sources in one call, explicitly distinguishing itself from open-web search. It also differentiates from sibling ask_pipeworx by naming the alternative and the condition for choosing it. The verb, resource, and scope are all concrete and immediately actionable.

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 gives explicit when-to-use guidance: broad/multi-part questions over structured data, and explicitly says to use ask_pipeworx for a single lookup or when not signed in. It also notes that depth:'thorough' requires a paid plan, adding a practical selection constraint. This fully equips an agent to route between deep_research and its siblings.

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