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

Beyond the annotations (readOnly, openWorld, idempotent), the description reveals the refusal mechanism, return shape with evidence verbatim, refusal reason enum, and the extra LLM call cost. It also explains that answers are extracted only from tool results, which is a key behavioral trait not captured by annotations. 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 well-organized: it front-loads the core purpose, then details the mechanism, return/refusal formats, usage guidance, and cost. A couple of phrases could be trimmed, but every sentence carries distinct information and the structure aids quick scanning.

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?

Because there is no output schema, the description bears the full burden of explaining return values—and it does so in detail, providing both success and refusal shapes with enum values. It also covers the tool's routing scope, cost implications, and high-stakes use cases, making it fully sufficient for an agent to invoke and interpret results.

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%—all six parameters are aliases for 'question' and the schema already describes them fully. The description adds no additional parameter semantics, which is acceptable given the high schema coverage, 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, discriminative phrase ('Hallucination-resistant answer mode for high-stakes reads') and explicitly positions itself against the sibling ask_pipeworx by explaining it uses the same routing but then grounds the answer in tool output. This allows an agent to immediately distinguish it from ask_pipeworx and ask_pipeworx_beta without inspecting schemas.

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?

It gives explicit when-to-use conditions ('whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts') and when-not-to-use guidance ('prefer ask_pipeworx for casual lookups'), including a cost tradeoff. This is textbook usage guidance—clear, specific, and actionable.

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