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

The annotations already establish readOnly, openWorld, idempotent, and non-destructive behavior, and the description adds substantial context beyond them: account and paid-plan requirements, expected latency, parallel decomposition, findings packet contents, gaps[] for unanswered facets, contradictions[] for standard/thorough, semantic excerpting, and citation_uri resolvability. This is unusually transparent behavior disclosure.

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, but every sentence carries operational value: auth constraints, sibling routing, source scope, output structure, depth semantics, latency, and caveats about open-web content. It is front-loaded with the critical account requirement and fallback tool, and contains no filler.

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 the tool's complexity, absence of an output schema, and rich sibling set, the description is remarkably complete. It covers required authentication, pricing, expected output shape, timing, source scope, multi-hop behavior, and failure semantics, leaving an agent with enough context to invoke and interpret the tool 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%, so the schema already documents both parameters well. The description nevertheless adds meaning by explaining that question accepts broad natural-language multi-part queries, and that depth controls facet count and follow-up hops — beyond what the enum names alone communicate.

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 names a specific verb and resource: it performs grounded multi-source research across Pipeworx's 1,497 structured data sources, decomposes questions into facets, and routes them to 5,724 tools in parallel. It also explicitly distinguishes itself from open-web search and from ask_pipeworx, making its purpose unmistakable.

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 and when-not-to-use guidance: use it for broad/multi-part structured-data questions, use ask_pipeworx for single lookups, use ask_pipeworx for breaking/colloquial current news, and use ask_pipeworx if not signed in. It also explains depth-level tradeoffs, so an agent can select it correctly against sibling tools.

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