think_verify

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

With no annotations, the description carries the full burden and does well: it discloses that the verifier runs in a fresh context that never saw the reasoning chain, that optional N self-consistency samples are run, and that without a configured layer nothing executes and only prompts return. It omits any permission, cost, or side-effect notes, but the core behavioral profile is clear.

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 behaviorally most important fact (independent verification in a fresh context) is front-loaded, and the server_side toggle and fallback follow. The single dense paragraph is clause-heavy but every clause conveys a distinct behavior.

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?

An output schema exists, so return values need not be documented, and the description covers the server-side vs host-side execution split and the degraded no-layer mode. Undocumented input parameters (problem, conclusion, evidence) are the remaining gap.

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 0% across 6 parameters, so the description must compensate. It explains server_side and samples meaningfully and implicitly references the adversarial counter, but problem, conclusion, and evidence remain undefined in both places. Partial compensation only.

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?

States a specific verb and resource: 'Verify a conclusion independently'. The adversarial refutation and self-consistency sampling detail makes the operation concrete. It stops short of distinguishing itself from the closely named sibling think_verify_submit, which an agent would likely confuse it with.

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?

Gives clear usage conditions: with a configured LLM layer it runs server-side automatically, and setting server_side=false returns prompts for the host to run. It also covers the no-layer fallback. It does not state when to choose this over think_verify_submit, so sibling routing is left to inference.

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