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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, which cover safety. The description adds substantial behavioral context beyond that: it costs an extra LLM call, returns a structured refusal with specific reason codes, and guarantees the answer is extracted only from tool results. This is rich, non-obvious behavior that the agent needs to know, and it does not contradict the 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 well-structured and front-loaded. The first sentence states the core purpose, followed by the routing mechanism, the return format, the usage guidance, and the cost comparison. Every sentence earns its place; there is no filler. The length is justified by the complexity of the tool's behavior and refusal reasons.

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 must explain the return structure, which it does thoroughly (including refusal reasons and their meanings). It also covers the cost implication and the exact scenarios where this tool is required. Nothing an agent needs to correctly invoke this tool 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%: all six parameters are documented as aliases for the question, and the description itself states 'Your question in natural language.' The description adds no new parameter semantics beyond the schema; it merely restates that aliases are accepted. With full schema coverage, baseline 3 is appropriate.

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 verb+resource: 'Hallucination-resistant answer mode for high-stakes reads.' It clearly distinguishes itself from the sibling ask_pipeworx by describing the extraction mechanism ('EXTRACTS the answer using ONLY what the tool result contains') and the refusal behavior. An agent can instantly tell what this tool does and how it differs from its sibling.

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?

Explicitly states when to use ('whenever an answer will be quoted, cited, or acted on... must not invent facts') and when not to ('prefer ask_pipeworx for casual lookups'). It also names the alternative (ask_pipeworx) and the cost trade-off. No ambiguity remains.

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