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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, which already signal a safe read operation. The description goes beyond this by detailing the exact success return shape and, importantly, the refusal behavior with specific reasons (not_in_source, no_tool_match, etc.). It also discloses the extra LLM call cost. 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 compact yet information-dense. It front-loads the core purpose, then layers routing, return contract, refusal behavior, use cases, and cost tradeoff. Every sentence earns its place, and the structure moves from what it does to when to use it without fluff.

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 compensates by explicitly spelling out the return format on both success and refusal paths. It also covers the key decision factor (cost) and the exact conditions for use. An agent has everything needed to invoke this tool correctly and interpret its response.

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%, with all six parameters documented as aliases for the single required 'question' field. The description refers to 'fills arguments' but adds no parameter-level detail beyond what the schema already states. Baseline 3 applies because the schema carries the semantic weight, 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 opens with a specific purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It then clarifies the mechanism (same routing as ask_pipeworx, extract using only tool result) and distinguishes itself from its sibling ask_pipeworx by noting the extra LLM call. An agent can clearly tell this apart from the other ask_pipeworx variants and general research tools.

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?

Explicit usage guidance is provided: 'Use whenever an answer will be quoted, cited, or acted on' and 'prefer ask_pipeworx for casual lookups.' This gives an exact condition for use plus a named alternative with the tradeoff (cost). No inference is required.

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