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

Annotations already mark it readOnly, openWorld, and idempotent; the description goes well beyond them by disclosing parallel facet routing, explicit gaps[] with 'never invented' behavior, fetchable citation_uri conditions, semantic excerpting, contradiction scanning, and expected latency. No contradiction with the readOnly/openWorld/idempotent annotations.

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 nearly every clause carries a distinct fact: auth requirement, alternative routing, data scope, parallel decomposition, output fields, depth-specific behavior, citation fetchability, and timing. It is somewhat dense and run-on, but it front-loads the routing-critical account requirement and earns its length for a tool this complex.

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?

There is no output schema, so the description compensates by enumerating the findings packet fields: verbatim evidence, confidence, source, fetched_at, hop, citation_uri, gaps[], and contradictions[]. It also covers account/plan requirements, latency expectations, and the sibling routing decision, leaving an agent with what it needs to invoke and interpret the call.

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?

The schema already describes both parameters (100% coverage), and the description adds significant semantics: depth is expanded with exact facet counts (quick=3, standard=3, thorough=6), the paid requirement for thorough, and hop/gap-recovery/contradiction behavior. The question parameter is contextualized with natural-language examples and multi-part decomposition.

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 verb and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources... in ONE call.' It explicitly distinguishes itself from open-web search and names ask_pipeworx as the alternative for single lookups, so an agent can tell it apart from siblings.

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?

It gives explicit when-to-use guidance: 'Best for broad/multi-part questions over structured data' with concrete example queries. It also gives a when-not-to-use rule, 'For a single lookup use ask_pipeworx instead,' plus an account prerequisite that routes unsigned-in users to ask_pipeworx.

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