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

Annotations already establish read-only/open-world/idempotent behavior, and the description adds valuable beyond that: refusal contract with exact refusal_reason values, evidence extraction rule, 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?

Three sentences are dense but each earns its place: mode and routing, return/refusal contract, then when to use and cost trade-off. Key caveats are 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?

Though there is no output schema, the description fully specifies the success and refusal shapes, making the return contract explicit. It resolves ambiguity vs ask_pipeworx and ask_pipeworx_beta and covers the extra-call cost, so an agent has the information needed to invoke 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 has 100% coverage; the description points to natural language questions and alias handling without adding much parameter-level detail beyond the schema. This is the baseline expected when the schema fully documents the single required parameter.

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 by naming the tool as a 'hallucination-resistant answer mode' and specifies exact behavior: route across 5,708 tools, fill arguments, fetch data, extract answer only from tool result. It differentiates from ask_pipeworx by emphasizing grounded extraction with evidence and explicit refusals.

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 when-to-use conditions: 'whenever an answer will be quoted, cited, or acted on' and where invention is unacceptable. It also states the trade-off ('costs one extra LLM call') and directs casual lookups to ask_pipeworx, an explicit alternative.

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