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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds significant non-obvious behavior: account/paid-plan requirements, latency ranges, gaps[] never invented, contradictions[] for standard/thorough, citation_uri only present when fetchable, and semantic excerpting. There is no contradiction between the description and 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 dense and information-rich, front-loading the account requirement and core scope before moving into comparisons and depth semantics. It is longer than ideal, but nearly every sentence carries operational value, with only minor redundancy around depth behaviors and contradictions.

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 carries the burden of explaining return shape, and it does: findings packet fields, verbatim evidence, confidence, source, fetched_at, pipeworx:// citations, gaps[], contradictions[], hop field, and citation_uri fetchability. It also covers prerequisites, latency, and when the tool will underperform, so an agent has enough context to invoke it appropriately.

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?

The input schema already documents both parameters at 100% coverage, so the description does not need to restate basics. It adds real value beyond the schema by explaining operational differences between 'quick', 'standard', and 'thorough' (hop counts, gap recovery, contradictions scan, paid plan, latency), and by clarifying that the question parameter supports broad multi-part phrasing.

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 uses a specific verb ('grounded multi-source research') with a named resource (Pipeworx's 1497 structured data sources) and explicitly contrasts itself with 'NOT open-web search' and ask_pipeworx. It clearly communicates that this tool decomposes questions, routes facets across tools, and returns a findings packet, distinguishing it from sibling lookups.

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 explicitly says when to use this tool ('best for broad/multi-part questions over structured data'), names the alternative for single lookups ('For a single lookup use ask_pipeworx'), and excludes breaking/current-news topics in favor of ask_pipeworx. It also gives an auth-based routing rule: if not signed in, use ask_pipeworx instead.

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