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

Annotations already cover read-only, open-world, idempotent, and non-destructive traits. The description adds meaningful behavioral detail beyond annotations: refusal reasons, that answers are extracted only from tool results, the returned field structure, and 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?

Dense but well-structured: starts with the core purpose, then return behavior, refusal cases, usage guidance, and cost tradeoff. Every sentence adds useful information and nothing is redundant with schema or annotations.

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 no output schema, the description fully documents the success response fields and all possible refusal reasons. It also explains when to prefer this over the sibling ask_pipeworx, making the tool self-sufficient for correct invocation.

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%, so the schema fully documents the 'question' parameter and its aliases. The description adds no additional parameter-specific semantics beyond saying the question is in natural language, which the schema already states.

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?

States a precise purpose: 'hallucination-resistant answer mode for high-stakes reads' that routes through tools then extracts answers only from returned data. Explicitly distinguishes itself from ask_pipeworx by describing the same routing but a stricter, evidence-grounded output mode.

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?

Gives explicit when-to-use guidance: 'whenever an answer will be quoted, cited, or acted on' and lists high-stakes domains. Also names the cost tradeoff and explicitly prefers ask_pipeworx for casual lookups, giving a clear alternative.

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