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

Even though annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, the description adds rich behavioral detail: account/paid-tier requirements, that gaps[] are never invented, that citation_uri is only present when resolvable, that large records are semantically excerpted (not head-truncated), that contradictions[] appear for standard/thorough, and explicit timing. Nothing contradicts 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 and a single dense paragraph, but virtually every sentence carries non-redundant information, and the critical differentiators (account requirement, not open-web, prefer ask_pipeworx for news/single lookups) are front-loaded. It could be structured with breaks for scannability, but the density is justified by the tool's complexity and sibling count.

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?

With no output schema present, the description fully covers the return contract: findings packet fields, hop field, citation_uri resolvability, gaps[] semantics, contradictions[], and semantic excerpting behavior. It also covers operational context (sign-in requirement, paid thorough tier, latency ranges), leaving no critical unknown for correct invocation or result interpretation.

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% with detailed enum descriptions already, so the baseline is 3. The description adds meaning beyond the schema by tying depth to the research narrative — explaining that standard/thorough resolve multi-step questions in one call via gap recovery and lead-chasing, and that contradictions[] only appear at those depths — which helps an agent choose a depth value operationally.

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 concrete resource (1517 STRUCTURED data sources fed by 5,801 tools in parallel), and an exact deliverable (findings packet with evidence, confidence, source, fetched_at, citation). It explicitly distinguishes itself from siblings by stating it is NOT open-web search and naming ask_pipeworx as the single-lookup alternative, so an agent can tell it apart without opening other schemas.

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?

When-to-use is explicit and multi-layered: 'Best for broad/multi-part questions over structured data', with direct when-not guidance — use ask_pipeworx for single lookups, for breaking/colloquial current-news topics, and if the user is not signed in. It also gives latency expectations (15-60s, thorough up to ~90s) and explains how depth maps to question complexity.

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