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

The description goes far beyond the annotations by disclosing account requirements, paid-plan restrictions, latency expectations (15-60s, up to ~90s for thorough), the fact that gaps[] are explicitly returned and never invented, that citations are stable and fetchable via citation_uri, that contradictions[] appear for standard/thorough, and that large records are semantically excerpted. This is rich behavioral context that the annotations alone do not provide.

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 densely informative: nearly every sentence contributes selection, invocation, or behavior context, including auth, limitations, response shape, and latency. It is front-loaded with the account requirement and the most important alternative (ask_pipeworx). A single run-on paragraph and some repetition ('For a single lookup use ask_pipeworx...') prevent a perfect score.

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 and the absence of an output schema, the description supplies a thorough mental model of the return packet: verbatim evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[], hop fields, and excerpt behavior. It also covers auth prerequisites, pricing, latency, and alternative tools, so an agent has everything needed to decide when to call it and what to expect.

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%, and the schema already documents the question parameter well and gives a detailed enum description for depth. The tool description adds some behavioral context for depth (e.g., second-hop iteration, gap recovery, contradictions) but largely repeats or paraphrases what the schema already states. It does not add substantial new parameter-level semantics beyond the schema.

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 clearly identifies the tool as grounded multi-source research across Pipeworx's 1506 structured data sources, with a specific verb ('research'), resource, and mechanism (decomposes question into facets and routes them in parallel). It also explicitly differentiates itself from siblings: it is 'NOT open-web search', and it is contrasted with ask_pipeworx for single lookups and current-news topics.

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?

Provides explicit when-to-use and when-not-to-use guidance: best for broad/multi-part questions over structured data; use ask_pipeworx instead for single lookups or breaking/current-news topics; if not signed in, use ask_pipeworx because it works on every tier. Depth selection is also tied to use cases (quick/standard/thorough) with concrete behavioral consequences.

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