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

Even though annotations already mark this as read-only, idempotent, and non-destructive, the description goes far beyond them: it details the findings packet structure (verbatim evidence, confidence, source, fetched_at, citation_uri), the explicit gaps[] behavior for unanswered facets, the contradictions[] scan, semantic excerpting of large records, and expected latency. It also states a negative behavior — 'never invented' — which is valuable trust context.

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 sentence adds distinct value — auth requirements, sibling differentiation, return semantics, latency, and negative behavior. It is front-loaded with the account requirement and the critical alternative tool. It loses a point for being a dense wall of text that could be better structured with bullets or clearer paragraph breaks, but it is not padded.

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 tool with no output schema, this description is remarkably complete: it covers prerequisites, expected latency, output fields, failure/gap behavior, contradictions, hop semantics, and how to choose between this and the most relevant sibling. An agent has enough grounding to decide whether to invoke deep_research and what to expect from the result.

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 baseline is 3; the description nonetheless adds meaningful parameter-level context beyond the schema. It explains the practical auth constraint for depth:'thorough' (paid plan), clarifies that question is natural-language and can be broad/multi-part, and elaborates how the depth levels drive second-hop gap recovery and lead-chasing behavior.

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 a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources' in one call, explicitly distinguishing itself from open-web search and from the ask_pipeworx sibling. It also gives concrete example use cases ('compare X and Y's regulatory + financial exposure'), making the tool's 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: it is best for broad/multi-part structured-data questions, while single lookups and breaking/current-news topics should use ask_pipeworx. It even covers the auth prerequisite ('If you are not signed in, use ask_pipeworx instead'), which is unusually actionable.

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