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

Beyond the readOnly/idempotent annotations, the description discloses grounding behavior, exact success and refusal return shapes, the refusal_reason enum, and the extra LLM call cost. It clearly states that the tool refuses when the data does not directly answer, which is critical for high-stakes use. No 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 but well structured: purpose, routing mechanism, return contract, refusal contract, use cases, and cost tradeoff. Every sentence carries decision-relevant information, and the most important aspect — hallucination resistance — is front-loaded.

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 fully explains what the tool returns: the success shape with evidence, confidence, source, and fetched_at, plus the refusal shape with concrete refusal_reason values. It also covers routing scope, aliases, and when to choose the sibling tool, leaving no essential gap for correct invocation.

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%, and the single semantic parameter (question) is fully documented in the schema, including alias fields. The description adds little beyond restating that natural-language questions and aliases are accepted, so the baseline 3 applies.

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 specific resource and behavior: a hallucination-resistant answer mode that routes through many tools, fetches data, and extracts answers only from tool results. It also distinguishes itself from the sibling ask_pipeworx by emphasizing grounded extraction, evidence, and refusal behavior.

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: answers that will be quoted, cited, or acted on, plus high-stakes domains like financial verdicts, legal claims, and medical lookups. It also says when not to use it — casual lookups — and names the preferred alternative, ask_pipeworx, along with the cost tradeoff.

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