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

Beyond the readOnlyHint and idempotentHint annotations, the description discloses concrete behavioral traits: it only extracts from tool result content, returns a structured success payload with verbatim evidence, and explicitly refuses with categorized refusal_reason values when data is insufficient. It also reveals an extra LLM call cost, which is critical for an agent deciding between tools. 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 dense but every sentence earns its place: mode, routing behavior, extraction guarantee, return/refusal contract, usage trigger, and cost trade-off. The most important distinction ('hallucination-resistant... high-stakes') is front-loaded, and the guidance against overuse closes the loop.

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?

Although there is no output schema, the description fully specifies the success shape and refusal shape, including refusal reasons. Given the range of sibling tools and the high-stakes use case, the description provides enough information for an agent to select and invoke the tool correctly. The only omitted details are parameter aliases, which the schema already covers.

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%, and the schema fully documents the `question` parameter plus its five aliases. The description adds no parameter-level detail, but at this coverage level the schema already carries the burden. The description's mention of 'fills arguments' is behavioral context, 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 clearly defines a specific capability: a hallucination-resistant answer mode for high-stakes reads that routes like ask_pipeworx but extracts answers only from tool results. It explicitly contrasts itself with ask_pipeworx, making the distinction between siblings obvious. The verb and resource are clear and actionable.

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 when answers will be quoted, cited, or acted on, especially for financial, legal, medical, or public-statement contexts. It also states when NOT to use it by saying to prefer ask_pipeworx for casual lookups. This is modelable decision guidance, not just vague context.

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