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

Annotations already declare readOnly, openWorld, and idempotent hints, and the description adds substantial operational context beyond them: account/paid-tier requirements, parallel decomposition across 5,798 tools, gaps[] for unanswered facets, never-invented guarantees, citation_uri resolvability, contradictions[] behavior, excerpting semantics, and expected latency. No contradiction with annotations exists.

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 it is information-dense and each clause covers a distinct operational concern: account, fallback, source types, execution model, output packet, depth semantics, citations, contradictions, truncation behavior, and timing. There is slight redundancy (e.g., 'NOT open-web search' echoed), but the density justifies the length.

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 there is no output schema, the description thoroughly compensates by explaining the return shape: findings packet with verbatim evidence, confidence, source, fetched_at, stable pipeworx:// citations, gaps[], contradictions[], hop field, and citation_uri. It also covers account requirements, depth variations, and expected latency, so an agent can both invoke and interpret the result correctly.

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% and the schema already documents both `question` and `depth` with meanings. The description adds value beyond the schema by tying depth values to paid tiers, hop counts, gap-recovery behavior, contradictions[] scans, and realistic time ranges, and by affirming that broad/multi-part questions are appropriate for `question`.

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 states a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources in ONE call.' It explicitly distinguishes itself from open-web search and from ask_pipeworx, so an agent can tell it apart from siblings without reading their 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?

It gives explicit when-to-use guidance ('Best for broad/multi-part questions over structured data') and clear exclusions: 'For a single lookup use ask_pipeworx instead' and 'If you are not signed in, use ask_pipeworx instead.' It also gives concrete example queries, leaving no ambiguity about selection.

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