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

Beyond read-only/idempotent annotations, the description discloses the exact success shape ({answer, evidence, confidence, source, fetched_at, refusal_reason:null}), the refusal reasons, and that it costs one extra LLM call. 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?

The description is dense but each sentence carries distinct information: mode, behavior, outputs, refusal semantics, usage context, and cost tradeoff. It is front-loaded with the most important characteristic.

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 fully specifies return values and error/refusal states. Given the simple one-parameter input and rich annotations, nothing essential is missing for an agent 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%, so the schema already defines the single question parameter and aliases. The description adds context about routing and extraction but does not need to add parameter detail; baseline 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 states a specific purpose: a hallucination-resistant answer mode that extracts answers using only tool result content. It explicitly differentiates itself from ask_pipeworx by the evidence-backed, refusal-capable behavior, so an agent can distinguish it from siblings.

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 guidance ('Use whenever an answer will be quoted, cited, or acted on...') and an explicit when-not-to-use ('prefer ask_pipeworx for casual lookups'). It also names the alternative ask_pipeworx 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.