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

Even though annotations already show readOnlyHint, openWorldHint, and idempotentHint, the description discloses much more: exact success and refusal return shapes, the five enumerated refusal_reason values, and the constraint that answers use only tool-result content. It also reveals 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?

Dense but efficient: every sentence carries necessary operational information — mode, routing, extraction rule, output contract, refusal reasons, use cases, and cost comparison. The highest-stakes facts are front-loaded in the first sentence.

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?

There is no output schema, so the description correctly supplies the full return contract including success fields, evidence as a verbatim quote, and all refusal reasons. It also addresses the main routing decision vs its sibling and the cost implication, making it complete for an agent deciding whether and how to call it.

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% and fully documents the question field plus five aliases, so the baseline is 3. The description adds meaning beyond the schema by explaining how the question is processed: it routes across 5,724 tools, fills arguments, and only extracts from the fetched result. This context helps agents craft questions appropriately even though the raw parameter definition is already complete.

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?

Opens with 'Hallucination-resistant answer mode for high-stakes reads,' immediately establishing a specific verb and purpose. It explicitly differentiates from the sibling ask_pipeworx by describing the extraction-only-from-tool-result behavior and the refusal contract, so an agent can tell them apart.

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?

States exactly when to use it: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also names the alternative and the opposite condition: 'prefer ask_pipeworx for casual lookups,' plus a concrete 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.