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

The annotations already declare read-only, open-world, idempotent, and non-destructive behavior. The description adds transparency about refusal behavior (explicit refusal reasons like 'not_in_source' and 'tool_error') and the grounding mechanism, which goes beyond annotations without contradicting them. A small deduction because the core behavior is already covered by 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 well-structured and conveys necessary information without excessive verbosity. It repeats the phrase 'picks the right tool...' and the return format, but these repetitions serve emphasis. It is reasonably concise for the amount of detail provided.

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?

The description explains the return format (answer, evidence, confidence, source, fetched_at, refusal_reason) and the refusal reasons, even though no output schema is provided. It also gives cost comparison with the sibling tool. This is sufficient context for an agent to use the tool correctly. A small deduction because the return format could be considered part of the tool's implicit contract, but it's helpful to state it explicitly.

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 six aliases for the 'question' parameter. The description explicitly states 'Your question in natural language' and lists all aliases, clarifying that any of them can be used interchangeably. This adds meaning beyond the raw schema, though the parameter semantics are simple.

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 states the tool's purpose: a hallucination-resistant answer mode for high-stakes reads, with explicit mention of grounding answers in tool results. It also distinguishes itself from the sibling ask_pipeworx by emphasizing the grounded extraction approach.

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?

The description provides explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also gives a when-not-to-use condition: 'prefer ask_pipeworx for casual lookups,' citing the extra LLM call cost. This is clear and actionable.

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