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

The description fully discloses return shape (answer, evidence, confidence, source, fetched_at, refusal_reason) and the explicit refusal reasons (not_in_source, no_tool_match, tool_error, data_truncated, llm_error). It also states that only tool result content is used, which aligns with and reinforces the readOnly, idempotent, non-destructive annotations. No contradiction exists.

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 front-loaded with the core purpose and provides necessary detail about behavior, output, and cost. It is slightly verbose with the repeated 'picks the right tool...' explanation and the restatement of use cases, but every sentence contributes useful information.

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?

Since there is no output schema, the description compensates by fully explaining the return structure, refusal modes, and supported aliases. It also covers operational context like cost and when to prefer another tool, 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% and the main parameter description explains it accepts aliases. However, the tool description adds little beyond the schema for the parameters themselves; the aliases are simply described as equivalent to question, which is already in the schema. Baseline 3 is appropriate given full coverage but no extra semantic depth.

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 is a hallucination-resistant answer mode for high-stakes reads, with the specific behavior of extracting answers only from tool results. It explicitly distinguishes itself from ask_pipeworx by adding evidence, confidence, source, and refusal behavior, making its purpose unique among siblings.

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 provides explicit guidance on when to use this tool: whenever an answer will be quoted, cited, or acted on and the agent must not invent facts. It also instructs to prefer ask_pipeworx for casual lookups, and notes the extra LLM call cost, giving clear decision criteria.

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