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

The description substantially extends the annotations by detailing refusal behavior, exact refusal reasons, the evidence quote mechanic, and the extra LLM call cost. It explains what happens when data doesn't directly answer—an explicit refusal—rather than silently guessing. This is rich behavioral disclosure beyond the readOnly/idempotent hints.

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 top line states the core value, the middle explains mechanics and output/refusal shapes, and the close gives selection guidance. It is dense but not bloated, and the key differentiator ('hallucination-resistant', 'high-stakes') is front-loaded.

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?

Even without an output schema, the description fully documents the success return object with all fields, the refusal object with its possible reasons, and the behavioral contract. Combined with annotations and full schema coverage, an agent has everything needed to select and invoke the tool correctly and interpret its result.

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 already explains that all 6 parameters are aliases for a natural-language question. The description adds no new parameter-level meaning beyond reinforcing that a question is expected. Baseline 3 is appropriate because the schema carries the full burden and the description does not need to compensate.

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 states a precise purpose: a hallucination-resistant answer mode for high-stakes reads that extracts answers only from the tool result. It clearly distinguishes itself from ask_pipeworx by naming the sibling and explaining the difference in output guarantees. The phrasing 'Hallucination-resistant answer mode' is specific and action-oriented.

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 says when to use the tool: 'whenever an answer will be quoted, cited, or acted on' and when not to: 'prefer ask_pipeworx for casual lookups.' It names the alternative (ask_pipeworx) and provides a cost-based selection criterion. This is direct, actionable routing guidance.

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