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

Annotations already cover safety (readOnly, idempotent, openWorld, non-destructive), so the bar is lower — yet the description adds rich behavioral context beyond them: the EXTRACTS-ONLY-from-tool-result constraint, the verbatim evidence quote, the five explicit refusal reasons (not_in_source, no_tool_match, tool_error, data_truncated, llm_error), and the extra-LLM-call cost. 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?

The description is long (~120 words), but every sentence earns its place given the tool's dual return modes, five refusal reasons, and cost tradeoff. The core concept is front-loaded in the first sentence, and the dense middle section conveys genuinely necessary behavioral detail rather than filler.

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 burden of explaining return values — and it does: both success and refusal shapes are spelled out, evidence/verbatim-quote semantics are disclosed, and the routing behavior across 5,908 tools is summarized. An agent has everything needed to invoke it correctly and interpret its result.

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 description coverage is 100% and the schema itself thoroughly documents the question parameter, including all six aliases (query, q, prompt, text, input). The description adds no parameter-specific semantics, but at full schema coverage the baseline of 3 applies — the schema already carries the load.

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 specific, differentiated verb phrase — 'Hallucination-resistant answer mode for high-stakes reads' — and immediately contrasts with ask_pipeworx, naming the sibling it is not. It also specifies the exact output contract (answer, evidence, confidence, source, fetched_at) and the refusal contract, leaving zero 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?

Explicitly states when to use it ('whenever an answer will be quoted, cited, or acted on... financial verdicts, legal claims, medical lookups, public statements') and gives a concrete exclusion: 'prefer ask_pipeworx for casual lookups,' including the cost rationale (one extra LLM call). This is textbook when/when-not/alternative guidance.

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