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

Annotations already convey read-only/idempotent behavior, but the description adds substantial behavioral context: grounded extraction, explicit refusal reasons, no fact invention, and additional LLM 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?

Somewhat long but well structured, front-loading the key distinction (grounded vs. casual) and then detailing return behavior and refusal reasons. The detail is justified by the tool's purpose.

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?

No output schema exists, but the description fully explains the return format, evidence quote, confidence, source, and refusal reason values. An agent has enough context to invoke and interpret 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% with a clear description of the question parameter and all aliases. The description adds no new semantic information beyond the schema, so 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?

States a specific purpose (grounded, hallucination-resistant answering) and distinguishes itself from ask_pipeworx by emphasizing answer extraction strictly from tool results. Clearly differentiates 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?

Explicitly says when to use (answers that will be quoted, cited, or acted on; no invented facts) and when not to (casual lookups, prefer ask_pipeworx). Also notes the extra LLM call cost.

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