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

Annotations already declare readOnlyHint/idempotentHint/openWorldHint, and the description adds substantial behavioral context beyond them: the refusal contract with enumerated reasons (not_in_source, no_tool_match, tool_error, data_truncated, llm_error), the verbatim evidence field, the confidence/source/fetched_at return fields, and the guarantee that answers are extracted only from tool results. This meaningfully enriches what the annotations alone convey.

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 long but every sentence earns its place: mode definition, mechanism, success shape, refusal shape, use cases, cost trade-off. It is front-loaded with the key differentiator and follows a logical flow. Slightly verbose because the full JSON return shapes are spelled out inline, but that detail is justified given the absence of an output schema.

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 correctly documents the full return contract on both success and refusal paths, which is essential for correctness given the tool's high-stakes use case. It covers purpose, mechanism, output, refusal modes, use cases, cost, and the preferred alternative. Nothing an agent needs to invoke 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 schema already documents the question parameter and all six aliases (query, q, prompt, text, input). The description does not add parameter-specific semantics beyond the schema's own documentation. Baseline 3 is appropriate since the schema carries the full burden; the description's mention of routing/argument-filling describes internal behavior, not the parameter's meaning.

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 opens with a specific, differentiating verb phrase: 'Hallucination-resistant answer mode for high-stakes reads.' It clearly states what the tool does (routes, fills arguments, fetches, then extracts the answer using ONLY the tool result), and explicitly distinguishes it from the sibling ask_pipeworx by naming the relationship ('Same routing as ask_pipeworx'). An agent can select this tool over its siblings 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?

Provides explicit when-to-use guidance with concrete domains ('financial verdicts, legal claims, medical lookups, public statements') and an explicit when-not-to-use rule with the alternative named ('prefer ask_pipeworx for casual lookups'). Also discloses the cost trade-off (one extra LLM call), giving the agent a decision criterion. No exclusion condition is left to inference.

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