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

Annotations already declare readOnly, idempotent, and non-destructive hints, but the description adds substantial behavior beyond that: it discloses the extraction policy ('using ONLY what the tool result contains'), the exact success return shape, and the full set of refusal reasons. It also reveals the additional LLM call cost, which is not inferable from annotations or schema.

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 earns its place: definition, mechanism, return/refusal contract, usage guidance, and cost tradeoff. It is front-loaded with the key differentiator and uses compact enumeration for refusal reasons rather than verbose prose. Nothing is redundant.

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?

Despite having no output schema, the description fully specifies the success and refusal return structures, including field names and refusal reason enums. It covers when to use the tool, how it behaves, and what cost to expect. An agent has everything needed to decide and invoke correctly.

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% — every property is documented as an alias for the question parameter. The description adds no parameter-level semantics beyond saying the tool 'fills arguments' internally, which is about routing, not the input parameter. Baseline 3 is appropriate when the schema fully documents 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 opens with a precise definition — 'Hallucination-resistant answer mode for high-stakes reads' — and explains the full mechanism: routing, tool selection, argument filling, fetching, and extraction restricted to tool results. It distinguishes itself from ask_pipeworx by the grounded/evidence-based behavior and by naming the sibling explicitly, so an agent can tell them apart without opening 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?

Explicit when-to-use guidance is provided: 'Use whenever an answer will be quoted, cited, or acted on' with concrete domains (financial, legal, medical, public statements). It also states when NOT to use it — 'prefer ask_pipeworx for casual lookups' — and names the alternative, 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.