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

Annotations already mark read-only/idempotent/non-destructive, and the description adds the exact success and refusal response shapes, refusal_reason enum, evidence quoting, 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?

Every sentence adds operational value: mode definition, routing mechanics, response contract, refusal contract, use cases, and cost comparison. It is dense but not padded, and the central purpose is front-loaded.

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 thoroughly explains return fields and refusal reasons, names the sibling it differs from and the cost tradeoff, and gives concrete high-stakes examples. An agent has enough to select and 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?

The schema already covers all six parameters (100% coverage) with question plus five documented aliases. The tool description adds no additional parameter meaning, so the baseline of 3 is appropriate.

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 specifies it extracts answers using only fetched tool results, returning evidence and refusals. This clearly differentiates it from ask_pipeworx and ask_pipeworx_beta by naming the same routing but distinct grounded extraction 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?

It gives explicit guidance: use when an answer will be quoted, cited, or acted on and facts must not be invented, while preferring ask_pipeworx for casual lookups because it costs an extra LLM call. This is as explicit as usage guidance gets.

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