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

Beyond the annotations, the description discloses the full success return shape including evidence as a verbatim quote, confidence, source, and fetched_at. It also enumerates all refusal_reason values (not_in_source, no_tool_match, tool_error, data_truncated, llm_error) and states that the answer only uses tool result content. The extra LLM call cost and routing mechanism are also transparently disclosed.

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 relatively long but every sentence carries substantive information: purpose, mechanism, return values, refusal modes, usage guidance, and cost comparison. It is front-loaded with the core purpose and structured logically, though slightly verbose in the routing explanation.

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?

With no output schema, the description fully compensates by specifying both the success and refusal return shapes, including exact field names and enum values for refusal_reason. It also covers when to use, when not to, cost implications, and the routing behavior. Nothing an agent needs to invoke or interpret this tool correctly is missing.

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 description coverage is 100%: the only meaningful parameter, question, is fully described in the schema, and all five aliases are documented. The tool description does not add parameter-specific semantics, but given full schema coverage, 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 immediately identifies the tool as a 'Hallucination-resistant answer mode for high-stakes reads' and clearly distinguishes it from its sibling ask_pipeworx by explaining the extraction mechanism: 'EXTRACTS the answer using ONLY what the tool result contains.' The verb, resource, and differentiating behavior are all explicit.

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 states exactly when to use the tool ('Use whenever an answer will be quoted, cited, or acted on...') and when not to ('prefer ask_pipeworx for casual lookups'). It also names the alternative explicitly and provides the cost trade-off ('Costs one extra LLM call vs ask_pipeworx').

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