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

With annotations already declaring read-only, idempotent, and open-world behavior, the description still adds substantial context: the exact success contract {answer, evidence, confidence, source, fetched_at, refusal_reason:null}, a full refusal taxonomy (not_in_source|no_tool_match|tool_error|data_truncated|llm_error), and the extra-LLM-call cost. It also discloses the internal routing over '5,738 across 1499 sources' and constrains answers to what the tool result contains.

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 dense but every sentence carries distinct information: purpose, mechanism, success contract, refusal contract, usage rule, and cost tradeoff. It is front-loaded with the core purpose and efficiently contrasts with ask_pipeworx; it runs slightly long, but the contract details are justified by the absence of an output schema.

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?

The tool is moderately complex (routing, grounding, refusal paths) and has no output schema, so the description carries the full burden of return-value disclosure — and it delivers: success shape, refusal reasons, and evidence semantics are all spelled out. Combined with rich annotations and 100% schema coverage, nothing an agent needs to select and invoke this tool 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 description coverage is 100%, so the schema fully documents all 6 parameters including the question aliases. Per the baseline for high coverage, the description need not repeat parameter details; it does enrich context by framing what kinds of questions suit the grounded mode, but adds no parameter-level semantics 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?

The opening phrase 'Hallucination-resistant answer mode for high-stakes reads' states a specific verb and resource with a clear differentiator. It explicitly names the sibling ask_pipeworx and pinpoints the distinction: 'EXTRACTS the answer using ONLY what the tool result contains' — an agent can tell this mode apart from its sibling without opening the schema.

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?

Gives an explicit when-to-use rule: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' with concrete domains (financial verdicts, legal claims, medical lookups, public statements). It also states the when-not and the alternative: 'prefer ask_pipeworx for casual lookups,' backed by the cost tradeoff of one extra LLM call.

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