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

Beyond the readOnlyHint annotation, the description discloses the refusal contract with specific refusal_reason values, promises a verbatim evidence quote, explains that data may be truncated, and reveals the extra LLM call cost. This gives the agent a precise mental model of failure modes and safety behavior.

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?

Every sentence earns its place: the core behavior, return contract, refusal reasons, usage guidance, and cost comparison are all packed into a compact paragraph. The critical 'use only what the tool result contains' constraint is front-loaded before the return details.

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?

Given there is no output schema, the description fully covers the return shape and refusal reasons. It also supplies routing guidance, cost tradeoffs, and a list of high-stakes use cases. An agent has everything needed to decide when to call it and how to interpret its response.

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?

The schema already documents the question parameter and all five aliases with 100% coverage, so the description does not need to explain parameter semantics. The description adds no parameter-level detail beyond framing the input as a natural-language question, which is consistent with the baseline for a fully documented single-logical-parameter tool.

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 claim: 'Hallucination-resistant answer mode for high-stakes reads.' It then explains the exact pipeline — route to a tool, fill arguments, fetch data, and extract the answer only from the tool result — which clearly distinguishes this from ask_pipeworx and other siblings.

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?

The description explicitly states when to use it: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements).' It also gives the alternative: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.'

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