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buy_verified_burst

Resolve critical decisions with verifiable certainty. Only pay when the answer passes a quality check, using best-of-N sampling and self-consistency verification.

Instructions

Buy a verified inference burst at a hard/irreversible/low-confidence decision. Escalates to fast silicon, samples best-of-N, gates the answer through a verifier, and charges (x402) ONLY if it passes. Returns the verified answer + a receipt. Budget-capped per agent. Use when getting it wrong is costly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNobest-of-N sample count.
requestYesThe decision/question to resolve.
strategyNobest_of_n
verifierNoself_consistency
answer_keyNoOptional ["json","<field>"] or ["regex","<pat>"] to normalize answers.
Behavior5/5

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

With no annotations, the description fully carries the burden. It reveals key behaviors: escalation to fast silicon, best-of-N sampling, verification gating, conditional charging 'ONLY if it passes', returns verified answer + receipt, and budget cap. No contradictions or missing critical traits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is highly concise—four sentences, each earning its place. Front-loaded with the main purpose, it efficiently covers purpose, process, output, and usage context without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (5 parameters, no output schema, no annotations), the description provides a solid mental model: it explains the flow and output. However, it omits details about the answer_key parameter's role in normalization and does not explain the trade-offs between different strategy/verifier options. Still, it is largely complete for the core functionality.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers 60% of parameters (request, n, answer_key have descriptions; strategy and verifier have enums but no descriptions). The description adds minimal parameter-specific meaning beyond the schema (e.g., 'samples best-of-N' relates to n, 'gates through verifier' relates to verifier). It does not significantly enhance understanding of the parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Buy a verified inference burst at a hard/irreversible/low-confidence decision.' It uses specific verbs ('buy', 'escalates', 'samples') and resource ('verified inference burst'), making it unambiguous. Although no siblings are provided, the description is sufficiently unique.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly advises when to use: 'Use when getting it wrong is costly.' This gives clear context. However, it does not mention when not to use or list alternatives, which would be ideal but is not required given the lack of sibling tools.

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

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