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

Even though annotations already mark the tool readOnly, openWorld, idempotent, and non-destructive, the description adds substantial behavioral context beyond those flags: it discloses account requirements, paid depth tiers, latency expectations (15-60s up to ~90s), parallel tool routing, gap[] reporting, contradictions[] output, semantic excerpting behavior, and the guarantee that citations are resolvable. There is no contradiction with the 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 densely packed with decision-relevant information, and it is front-loaded with the most critical constraint (account/sign-in requirement) before the core definition. A few clauses could be tightened, but no sentence is purely filler; the length is justified by the tool's complexity.

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 that there is no output schema, the description carries the full burden of explaining what the agent will receive, and it does so thoroughly: evidence + confidence + source + fetched_at + citation_uri per finding, gaps[] for unanswered facets, contradictions[] for standard/thorough, and the lifetime/behavior of citations. It also covers input expectations, latency, account constraints, and edge cases like breaking news returning empty gaps.

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 both `question` and `depth` in detail. The description adds extra meaning by explaining what each depth level does operationally (quick=3 facets, standard adds gap recovery + contradictions scan, thorough adds iterative lead-chasing), by clarifying that broad/multi-part questions are fine, and by framing the output as a findings packet. This goes beyond merely repeating 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 names a specific verb (research), a specific resource (Pipeworx's 1497 structured data sources across SEC filings, FRED, FDA, USPTO, etc.), and a concrete mechanism (decomposes into facets, routes in parallel, returns a findings packet). It also explicitly distinguishes itself from open-web search and from ask_pipeworx, so an agent can tell it apart from its siblings without opening the schema.

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 direct when-to-use guidance: broad/multi-part structured-data questions like 'compare X and Y's regulatory + financial exposure' are ideal, while single lookups and breaking/colloquial current-news topics should go to ask_pipeworx instead. It also covers account tiers and offers ask_pipeworx as the fallback when not signed in, so alternative selection is explicit.

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