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

The description adds substantial behavioral detail beyond the annotations: the refusal mechanism with specific refusal_reason values, the verbatim evidence quote, the constraint of using only tool result contents, and the extra LLM call cost. This complements the readOnlyHint and idempotentHint without contradicting them.

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?

Each sentence earns its place: purpose, routing mechanism, return/refusal format, usage guidance, and cost tradeoff. The description is front-loaded with the core concept and avoids fluff while covering essential decision-making information.

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?

Despite having no output schema, the description fully specifies the return shape (answer, evidence, confidence, source, fetched_at, refusal_reason) and the refusal reasons. Combined with annotations and the single well-documented parameter, an agent has everything needed to select and invoke the tool correctly.

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?

The input schema already documents the 'question' parameter with full alias coverage (query, q, prompt, text, input), so schema coverage is 100%. The description does not add parameter-level details, so it stays at the baseline rather than adding extra semantic value.

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 then specifies the exact mechanism: it routes like ask_pipeworx, fetches data, and extracts the answer using only the tool result. This clearly distinguishes it from its sibling ask_pipeworx and states the specific resource and behavior.

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 says 'Use whenever an answer will be quoted, cited, or acted on' and 'prefer ask_pipeworx for casual lookups.' It also names the tradeoff ('costs one extra LLM call'), giving the agent a concrete rule for choosing between the two tools.

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