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

The description substantially exceeds annotation coverage: it discloses anti-hallucination behavior ('explicit gaps[] for facets the data couldn't answer (never invented)'), citation resolvability guarantees ('present only when the source emits one that resources/read can actually serve'), latency expectations (15-60s, up to ~90s for thorough), semantic excerpting rather than head-truncation, and the paid-tier gate for thorough. Annotations already mark this as read-only/idempotent, and the description adds rich operational context without contradicting them.

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 a dense, single wall-of-text paragraph with nested parentheticals (e.g., '(free — sign in via GitHub ... depth:"thorough" needs a paid plan)'), which hurts scannability. However, nearly every sentence earns its place: auth gate is front-loaded, timing, gap behavior, citation semantics, and depth differences all carry unique information. The length is justified by the tool's complexity, though formatting could be improved.

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 fully carries the burden of explaining return behavior — and it delivers: findings packet composition (verbatim evidence, confidence, source, fetched_at, pipeworx:// citation), gaps[], contradictions[], hop field, citation_uri resolvability, timing, auth requirements, and depth semantics. For a high-complexity tool with dangling-chunk async behavior, nothing operationally necessary to call it correctly 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 schema fully documents both `question` and `depth`; the description's added value is modest but real — it flags that thorough is paid (absent from the schema) and reinforces that multi-part questions are expected. These are useful operational supplements, but the schema already carries the descriptive burden, so baseline 3 applies.

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 opens with a precise statement of function: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources ... in ONE call' and explicitly disambiguates with 'this is NOT open-web search.' It names the deliverable (findings packet) and gives concrete example questions while distinguishing itself from sibling ask_pipeworx. A specific verb, resource, scope, and sibling differentiation are all present.

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?

Usage conditions are explicit and actionable: 'If you are not signed in, use ask_pipeworx instead' and 'For a single lookup use ask_pipeworx,' plus 'Best for broad/multi-part questions over structured data.' The description names the alternative tool, the condition that selects it, and the kind of question this tool is built for, 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.