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

Beyond the readOnly, openWorld, idempotent, and non-destructive annotations, the description fully discloses the behavioral contract: the exact success return shape (answer, evidence, confidence, source, fetched_at, refusal_reason:null), the explicit refusal shape with all five refusal_reason enum values, and the grounding constraint that the answer is extracted ONLY from what the tool result contains. It also discloses the extra LLM call cost, which annotations cannot 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 information-dense with no wasted words. It front-loads the core value proposition ('Hallucination-resistant answer mode'), then precisely lists the behavioral contract, return/refusal shapes, usage guidance, and cost trade-off. Every sentence earns its place, and the structure flows logically from behavior to usage.

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 no output schema, the description supplies a complete answer contract including both success and refusal payloads with reason enums. It also covers routing behavior, use cases, and the cost-based preference relative to ask_pipeworx. The only sibling not contrasted is ask_pipeworx_beta, but the direct comparison with ask_pipeworx covers the main decision point for an agent.

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 coverage is 100%, so the 'question' parameter and its aliases (query, q, prompt, text, input) are already well documented by the schema. The description adds contextual meaning by explaining that the question drives routing and argument filling ('fills arguments'), but it does not add syntax-level details beyond the schema. Baseline 3 is appropriate for fully covered parameters.

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 defines a specific verb and resource: a hallucination-resistant answer mode that routes through available tools and extracts grounded answers, explicitly noting 'Same routing as ask_pipeworx' but distinguished by using 'ONLY what the tool result contains'. This clearly separates it from at least the closest sibling, ask_pipeworx, and even hints at why ask_pipeworx_beta might exist as a separate variant.

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 gives explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts', with concrete high-stakes examples (financial verdicts, legal claims, medical lookups, public statements). It also states when NOT to use it ('prefer ask_pipeworx for casual lookups') and justifies this with an objective cost trade-off ('Costs 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.