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

The description discloses significant behavior beyond the annotations: it routes through thousands of sources, fills arguments, fetches data, and extracts answers using only tool-result content. It also specifies the exact refusal reasons (not_in_source, no_tool_match, tool_error, data_truncated, llm_error) and the extra LLM call cost, which is valuable beyond readOnlyHint/idempotentHint.

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 sentence earns its place: purpose, mechanism, return contract, refusal behavior, usage guidance, and cost tradeoff. It is front-loaded with the most important distinction ('hallucination-resistant') and ends with actionable routing advice.

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 provides a complete return contract including the success shape and all refusal reasons. It also covers when to use, when not to use, cost implications, and behavioral guarantees, so an agent has everything needed to select and invoke the tool 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 parameter descriptions already fully document the aliases for the single 'question' parameter. The tool description does not add much parameter-level detail beyond implying the input is a natural-language question, so the baseline score of 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 clearly identifies this as a 'hallucination-resistant answer mode for high-stakes reads' and distinguishes it from the sibling ask_pipeworx by emphasizing that answers are extracted using ONLY the tool result. The verb+resource relationship is explicit: this tool answers questions through grounded extraction rather than generation.

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 this tool: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' with concrete domains like financial verdicts, legal claims, medical lookups, and public statements. It also names ask_pipeworx as the alternative for casual lookups and explains the cost tradeoff ('extra LLM call').

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