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

Annotations only declare readOnly/openWorld/idempotent hints, but the description adds substantial behavioral context: account/auth requirements, paid-plan gate for 'thorough', latency expectations (15-60s, up to ~90s), gap handling ('never invented'), contradictions[] on certain depths, citation_uri presented only when actually fetchable, and semantic excerpting of large records. No contradiction with 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?

Long but densely packed; every sentence earns its place by covering authentication, scope, the open-web distinction, output packet fields, gap/contradiction semantics, citation fetchability, excerpting, and latency. It is front-loaded with the account prerequisite and the alternative tool, then the core action, then caveats. No fluff or repetition of the title.

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, the description fully carries the return-value burden: it specifies verbatim evidence, confidence, source, fetched_at, stable pipeworx:// citation, gaps[], contradictions[], hop field, and citation_uri fetchability. It also covers prerequisites, latency, depth trade-offs, and the alternative tool. An agent has everything needed to select and invoke the tool 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%, but the description goes far beyond the enum labels: it explains what each depth level actually means behaviorally (quick=3 single hop; standard=3 with gap recovery + contradictions; thorough=6 paid iterative hop), what kinds of questions to pass, and what to expect in the result (hop field, citation_uri). This adds real semantic value beyond the schema.

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 1517 structured data sources in one call, with facet decomposition and parallel routing to 5,798 tools. Explicitly distinguishes itself from open-web search and from sibling tools by noting 'this is NOT open-web search' and pointing single lookups to ask_pipeworx. No ambiguity about what the tool does.

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 when-to-use guidance: 'Best for broad/multi-part questions over structured data' with concrete example queries. Gives when-not-to-use and the alternative: 'For a single lookup use ask_pipeworx instead.' Also handles account-tier routing: 'If you are not signed in, use ask_pipeworx instead.' The conditions are unambiguous.

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