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

Annotations already declare readOnly/openWorld/idempotent/not destructive; the description goes far beyond by disclosing account requirements and paid tiers, facet decomposition, parallel routing to 5,743 tools, exact findings-packet fields (verbatim evidence, confidence, source, fetched_at, pipeworx:// citation), explicit gaps[] 'never invented,' hop fields and resolvable citation_uri, contradictions[] for standard/thorough, semantic excerpting, and latency expectations. This is exemplary behavioral disclosure.

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 every sentence earns its place — no fluff or restating schema. Critical operational facts (account requirement, fallback alternative, 'NOT open-web search') are front-loaded, followed by mechanics, return format, caveats, and timing. The density 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?

Despite having no output schema, the description fully explains the return packet, gaps[] behavior, contradictions[], citation_uri resolvability, hop fields, semantic excerpting, and expected latency. It covers auth, depth semantics, exclusions, and alternatives. Nothing needed to call the tool correctly and interpret results 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?

The schema already describes both parameters, and the description adds substantial meaning beyond it: it explains what 'depth' values actually do (quick single-hop, standard gap recovery + contradictions, thorough paid iterative hop with lead-chasing), and clarifies that 'question' should be broad/multi-part because decomposition is the point. This materially improves parameter understanding.

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 1500 STRUCTURED data sources... in ONE call.' It explicitly distinguishes itself from open-web search and from ask_pipeworx (single lookup vs. multi-source research), and provides concrete example questions. This makes the tool's purpose unmistakable.

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?

Provides explicit routing guidance: 'If you are not signed in, use ask_pipeworx instead'; 'For a single lookup use ask_pipeworx'; 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx.' It names the alternative tool and the specific conditions that select it, leaving nothing to inference.

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