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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint), the description discloses rich behavioral detail: the extraction uses ONLY tool-result content, the exact success return shape with evidence as verbatim quote, and the full refusal contract with five specific refusal_reason values. It also reveals the cost profile. This is exactly the kind of behavioral context annotations cannot carry. 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 longer than average but every sentence earns its place: purpose, routing mechanism, success shape, refusal contract, usage guidance, and cost trade-off. The key differentiator is front-loaded in the first clause. The inline refusal_reason enum is somewhat dense but necessary since no output schema exists to carry that information.

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?

This is a complex tool (routing across thousands of sources, hallucination-resistance guarantee, refusal behavior) with no output schema, so the description carries the full burden — and it delivers. Success and failure return shapes are both specified, safety is covered by annotations, and the cost/usage trade-off is stated. Nothing an agent needs to call this correctly or interpret its result 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% — the schema fully documents the single semantic parameter 'question' and all five aliases (q, text, input, query, prompt). The description adds no parameter-level meaning beyond what the schema provides, so the baseline of 3 applies. The mechanism detail ('fills arguments, fetches the data') is behavioral, not parameter semantics.

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 names a specific verb ('answer'), a precise mode ('hallucination-resistant'), and a concrete resource (data fetched from 5,743 tools across 1500 sources). It immediately distinguishes itself from the sibling ask_pipeworx by stating it shares routing but adds grounded extraction. An agent can tell exactly what this tool does and how it differs 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?

Explicit when-to-use guidance is given: '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 gives the exclusion: 'prefer ask_pipeworx for casual lookups' and discloses the cost trade-off (one extra LLM call). This fully routes the agent between siblings.

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