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

The description goes well beyond the readOnly/openWorld/idempotent annotations by specifying the exact success payload shape and the enumerated refusal reasons. It also discloses that answers are extracted only from the tool result and that the tool costs an extra LLM call. This gives the agent a realistic model of behavior without any 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 and front-loaded with the core purpose and differentiator. It is somewhat verbose with long parenthetical lists, but every sentence contributes either routing, return contract, or selection guidance. It could be tightened without losing value, but it is far from bloated.

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 without an output schema, the description fully specifies the return envelope and all failure modes, which is the most important missing context. It also provides sibling differentiation, cost tradeoffs, and explicit use cases, making the tool safe to invoke with minimal ambiguity.

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?

All six parameters are aliases for the same natural-language question, and the schema documents them with 100% coverage. The description adds no parameter-level semantics, but that is acceptable because the schema already carries the full burden. 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 clearly identifies the tool as a hallucination-resistant answer mode for high-stakes reads and immediately distinguishes it from ask_pipeworx. It specifies what it does: routes to a tool, fetches data, then extracts the answer only from the tool result. This gives the agent a precise, distinctive purpose.

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 states when to use the tool: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also says when not to use it: 'prefer ask_pipeworx for casual lookups,' and gives the cost rationale (one extra LLM call). This is clear, actionable routing guidance.

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