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

Annotations already flag readOnly/idempotent, and the description adds substantial behavior beyond: the facet-decomposition and parallel-routing process, the findings packet shape (verbatim evidence + confidence + source + fetched_at + citation, gaps[], contradictions[]), the "never invented" honesty guarantee, the citation_uri-only-when-fetchable caveat, and concrete latency expectations (15-60s, up to ~90s for thorough). It also discloses the account/paid-plan requirement, which annotations do not cover.

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?

Long but densely packed and logically ordered — auth constraint first, then identity (NOT open-web), then behavior, when-to-use, return format, and latency. Two minor redundancies (the ask_pipeworx routing and contradictions[] scanning are each stated twice) keep it from being maximally tight.

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 carries the full burden for return-value semantics and meets it: findings packet fields, gaps[], contradictions[], hop, and the citation-uri-only-when-fetchable caveat are all spelled out. For a tool of this complexity (parallel tool routing, tiered depth, latency, auth), nothing an agent needs to invoke it correctly or set expectations is missing.

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 documents both parameters, including per-depth facet counts. The description adds operational meaning the schema lacks: depth:"thorough" requires a paid plan (which should influence value selection), latency varies by depth, and example question phrasings demonstrate what a good `question` looks like. That pushes it modestly above the high-coverage 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?

States a specific verb and resource: "Grounded multi-source research across Pipeworx's 1540 STRUCTURED data sources ... in ONE call." It actively distinguishes itself from siblings by declaring "this is NOT open-web search" and routing single-lookup users to ask_pipeworx, so an agent can differentiate it at a glance.

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?

Gives explicit conditions: "If you are not signed in, use ask_pipeworx instead — it works on every tier" and "For a single lookup use ask_pipeworx instead." It also states when it is the right choice: "Best for broad/multi-part questions over structured data," backed by concrete example questions.

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