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

Annotations cover the safety profile (readOnly, idempotent, non-destructive), and the description adds substantial context beyond them: free-account requirement, paid-plan gating on depth:thorough, expected latency of 15-60s rising to ~90s for thorough, and the fact that it fans out across 7,176 tools in parallel. It also discloses failure semantics that matter operationally — gaps[] for unanswerable facets and mostly-empty gaps[] on non-catalog news topics — rather than inventing results. No conflict 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?

Front-loading is correct: the account gate and the ask_pipeworx fallback land in the first sentence, before any capability claims. It is dense and long — the depth/iteration explanation partially restates the depth parameter's own schema text — but nearly every sentence carries decision-relevant information, so the length is mostly earned.

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 carries the return-value burden itself, and it does: a findings packet with verbatim evidence, confidence, source, fetched_at, a resolvable pipeworx:// citation_uri, a hop field, plus gaps[] and contradictions[]. For a fan-out tool with nine parameters and a paid tier, nothing an agent needs to call it correctly or interpret the result is missing.

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 earns an extra point by explaining the depth tiers behaviorally (gap-recovery hop vs. lead-chasing hop, facet counts 3/6, contradictions[] only on standard and thorough) in terms an agent can weigh against its latency budget. The alias set is left entirely to the schema, which is the right division of labor.

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?

States a specific verb and resource — grounded multi-source research across 1863 structured data sources in one call — and explicitly negates the wrong reading with 'this is NOT open-web search'. It also names the exact sibling it is not (ask_pipeworx) and the conditions separating them, so an agent can discriminate it without opening any 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?

Gives explicit when-to-use ('broad/multi-part questions over structured data', with two concrete example questions), when-not ('for a single lookup use ask_pipeworx', 'for BREAKING or colloquial CURRENT-NEWS prefer ask_pipeworx'), and a prerequisite branch ('if you are not signed in, use ask_pipeworx'). Alternatives and exclusions are named outright rather than implied.

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