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

Beyond annotations, the description discloses the exact return shape on success and the refusal reasons on failure, the 'only what the tool result contains' extraction restriction, and the extra LLM call cost. These are meaningful behavioral details not derivable from 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 dense with useful context and front-loads the core concept. Slight redundancy exists between 'EXTRACTS the answer using ONLY what the tool result contains' and 'must not invent facts,' but overall every sentence contributes meaningful guidance.

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?

For a single-required-parameter tool with no output schema, this is complete: it explains the return format, refusal modes, cost, and when to use it versus the sibling. An agent has enough to invoke it correctly in high-stakes contexts.

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 description does not mention the question parameter, but the schema has full coverage with a clear description and aliases. With schema_description_coverage at 100%, the baseline applies; no additional parameter semantics are needed.

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 explicitly defines this as a 'hallucination-resistant answer mode for high-stakes reads' and explains it extracts answers using only tool-result content. It clearly differentiates from the sibling ask_pipeworx by stating 'Same routing as ask_pipeworx' but with the grounded extraction constraint.

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 gives explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on' and lists concrete high-stakes domains (financial verdicts, legal claims, medical lookups, public statements). It also provides the alternative: 'prefer ask_pipeworx for casual lookups' and explains the cost tradeoff.

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