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

Beyond the readOnly/openWorld/idempotent annotations, it discloses refusal behavior and exact refusal_reason values, the success return shape with verbatim evidence, 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 and front-loaded with purpose, then behavior, then usage guidance. The refusal_reason enum list is long but necessary for invocation correctness. Slightly long, but every sentence adds decision-relevant 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?

For a grounded QA tool with no output schema, the description fully specifies the return contract, failure modes, use cases, and cost tradeoff versus the sibling. An agent has everything needed to invoke it 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?

Schema coverage is 100% and the question parameter is already documented in the schema with aliases. The description merely says 'fills arguments' and does not add parameter-level semantics, so the baseline 3 applies.

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 verb and resource: 'Hallucination-resistant answer mode for high-stakes reads,' and contrasts itself with ask_pipeworx via 'Same routing as ask_pipeworx' while adding extraction-only behavior. This distinguishes it from the sibling ask_pipeworx without needing to inspect schemas.

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?

It states exactly when to use: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' and when not to: 'prefer ask_pipeworx for casual lookups.' It also names the alternative and the cost tradeoff ('Costs one extra LLM call'), leaving no ambiguity.

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