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

Annotations declare readOnly and idempotent hints, but the description adds substantial behavioral context: it explains the refusal contract, enumerates all refusal_reason values, discloses that extraction is limited to the tool result, and notes the extra LLM call cost. This goes well beyond what the annotations provide.

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 provides the return/refusal contract, usage guidance, and cost tradeoff. Every sentence contributes actionable information, and the structure makes the key distinctions easy to scan.

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?

There is no output schema, so the description fully compensates by specifying the success return shape and the refusal shape with exact reason strings. It also covers when to choose the alternative tool and what behavioral guarantees to expect, making the definition complete for an agent selecting and invoking the tool.

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 documents that all six parameters are aliases for the natural-language question. The description adds no additional parameter meaning beyond what the schema provides, so the baseline of 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 'Hallucination-resistant answer mode for high-stakes reads' and clearly identifies the tool's resource and action: it routes like ask_pipeworx but then extracts an answer using only the tool result. It differentiates itself from the sibling ask_pipeworx by emphasizing grounded, evidence-backed answers versus casual lookups.

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 this tool: whenever an answer will be quoted, cited, or acted on, and when the agent must not invent facts, with concrete domains like financial verdicts and legal claims. It also names the alternative ('prefer ask_pipeworx for casual lookups') and explains the cost tradeoff of one extra LLM call.

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