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

The description discloses behavior well beyond the annotations: it guarantees answers are extracted only from tool results, specifies the success payload with verbatim evidence and confidence, and enumerates explicit refusal reasons. This is valuable because annotations only mark the operation as read-only, open-world, and idempotent; there is no contradiction.

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 carries distinct value: the mode, the routing and extraction mechanism, the return/refusal contract, the high-stakes use cases, and the cost-based comparison with ask_pipeworx. It front-loads the core purpose before diving into response details, and has no filler.

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?

Even though there is no output schema, the description fully defines both success and refusal response shapes, explains when to use the tool, and names the preferred alternative with rationale. Combined with the annotations and schema, an agent has everything it needs to invoke the tool correctly and interpret its result.

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?

The input schema already documents the single required 'question' parameter and all aliases with 100% coverage, so the description does not need to repeat parameter details. The description adds useful context about how the question is routed and grounded, but it does not introduce parameter-level semantics beyond the schema, so the 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 states a concrete function: a hallucination-resistant answer mode that extracts answers only from fetched tool results, with explicit success and refusal shapes. It clearly distinguishes itself from ask_pipeworx by emphasizing grounded evidence and refusal behavior, so an agent can tell the tools apart without inspecting schemas.

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 says when to use it: high-stakes reads where answers will be quoted, cited, or acted on and the agent must not invent facts, citing financial, legal, medical, and public-statement examples. It also names the alternative, ask_pipeworx, and says to prefer that for casual lookups, including the cost tradeoff of one extra LLM call.

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