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

Beyond the readOnly, idempotent, and non-destructive annotations, the description discloses the refusal contract with five concrete refusal reasons, the evidence/verbatim-quote return behavior, and the extra LLM call cost. It also clarifies that answers come only from tool results, which is important behavioral detail.

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 but every clause earns its place: it covers purpose, behavior, return shape, refusal reasons, use cases, and cost trade-off. The most important identifier — hallucination-resistant/high-stakes — 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?

For a tool with one question parameter and no output schema, this description fully specifies the return contract, refusal contract, and usage boundaries. An agent has enough context to invoke it correctly and interpret both success and failure responses.

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% for the single question parameter and its aliases, so the schema already carries the parameter semantics. The description adds no additional per-parameter guidance, which is acceptable here because none is really 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 clearly identifies this as a hallucination-resistant answer mode for high-stakes reads and explains the answer-extraction behavior. It also names ask_pipeworx as the alternative it differs from, so an agent can distinguish it immediately.

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 explicitly states when to use the tool — when answers will be quoted, cited, or acted on and facts must not be invented — and when not to use it, preferring ask_pipeworx for casual lookups. This direct alternative routing is strong guidance.

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