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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the safety profile is set. The description adds substantial behavioral detail: it describes the return structure {answer, evidence, confidence, source, fetched_at, refusal_reason}, lists the specific refusal reasons (not_in_source, no_tool_match, tool_error, data_truncated, llm_error), and explains the extra LLM call cost. This goes beyond what annotations provide. 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 every sentence serves a purpose: it defines the mode, explains routing and extraction, enumerates the return structure and refusal reasons, states when to use it, and notes the cost tradeoff. It is front-loaded with the core 'hallucination-resistant' purpose and ends with practical usage guidance. No filler or redundancy.

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?

For a tool with a complex refusal system and a cost tradeoff relative to its sibling, the description covers all necessary aspects: the routing mechanism, extraction rule, the exact return and refusal formats, the scenarios that warrant use, and the cost consideration. Since there is no output schema, the description explains the return structure fully, making it complete for an agent to call 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 description coverage is 100% because all six parameters are aliases of the same 'question' field, and the schema already describes each alias. The description adds minimal value beyond the schema, only implicitly confirming that the question is in natural language. Baseline 3 is appropriate since the schema does the heavy lifting.

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: 'hallucination-resistant answer mode for high-stakes reads' and explains that it routes to a tool, fetches data, then extracts the answer from only the tool result. It contrasts with the sibling ask_pipeworx by highlighting the grounded extraction and refusal behavior, so an agent can distinguish it without opening the schema.

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 explicitly says when to use this tool ('whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts') and when not to ('prefer ask_pipeworx for casual lookups'). It also mentions the cost tradeoff ('one extra LLM call'), giving clear routing guidance relative to its sibling.

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