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

Beyond the annotations (readOnly, openWorld, idempotent), the description discloses the internal behavior: same routing as ask_pipeworx, fetching data, extraction constrained to the tool result, and explicit refusal reasons. It also documents the return contract and the cost tradeoff ('Costs one extra LLM call'), which is valuable behavioral context. 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 every clause earns its place: grounding behavior, return shape, refusal reasons, use cases, and cost tradeoff. It is front-loaded with the core distinction ('Hallucination-resistant') and the risk contexts follow naturally. This is efficient for the complexity it must communicate.

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 specifies the success payload ({answer, evidence, confidence, source, fetched_at, refusal_reason:null}) and the refusal payload with enumerated refusal_reason values. It also addresses when not to use it and the cost consequence. For a complex routed tool, nothing an agent needs to decide or invoke is missing.

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% for the six parameter aliases, all converging on 'question,' so the schema already does the heavy lifting. The description does not add parameter-level detail beyond saying the tool 'fills arguments' after routing, which is process rather than parameter semantics. 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 opens with a specific, contrastive purpose: 'Hallucination-resistant answer mode for high-stakes reads' and explains it extracts answers only from tool results. It explicitly ties to the sibling ask_pipeworx ('Same routing as ask_pipeworx') while distinguishing the grounded extraction behavior, so an agent can tell them apart.

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 an explicit when-to-use rule: 'Use whenever an answer will be quoted, cited, or acted on' with concrete domains (financial verdicts, legal claims, medical lookups, public statements). It also names the alternative for the opposite case: 'prefer ask_pipeworx for casual lookups.' This is exactly the guidance an agent needs.

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