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

Goes well beyond the readOnlyHint/idempotentHint annotations by disclosing the full behavioral contract: internal routing pipeline (5,798 tools, 1517 sources, argument filling, fetching), the success response shape with evidence as verbatim quote, and the explicit refusal taxonomy (not_in_source, no_tool_match, tool_error, data_truncated, llm_error). It also discloses the cost trade-off of an extra LLM call. 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 core purpose is front-loaded in the first sentence, and every subsequent sentence earns its place: differentiation from siblings, return contract, refusal reasons, usage policy, and cost trade-off. It is dense, but the refusal-code enumeration and cost note are operationally necessary for correct agent behavior rather than padding.

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 carries the full burden of return-value disclosure and fully meets it — detailing both the success shape ({answer, evidence, confidence, source, fetched_at}) and all refusal variants. Combined with 100% schema coverage for parameters and annotations covering the safety profile, an agent has everything needed to invoke and interpret this 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 description coverage is 100% — the schema fully documents the single conceptual parameter 'question' and its six aliases. The description adds contextual framing about what kinds of questions suit this mode (high-stakes reads) but no additional parameter syntax or format semantics, so the baseline of 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 purpose — 'Hallucination-resistant answer mode for high-stakes reads' — identifying a specific verb, resource, and behavioral constraint. It explicitly differentiates itself from the sibling ask_pipeworx by name ('Same routing as ask_pipeworx') and clarifies what makes this mode different: it extracts answers using ONLY what the tool result contains.

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: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with concrete examples (financial verdicts, legal claims, medical lookups, public statements). Also states the exclusion rule — 'prefer ask_pipeworx for casual lookups' — backed by a concrete cost rationale (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.