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verify_and_kernel_check

Verify reasoning and locally check the generated Lean proof for mathematical soundness. Returns a certificate and a kernel check that confirms validity on your own machine.

Instructions

Verify reasoning, then locally kernel-check the emitted Lean proof.

The complete trust loop in one call. If the verdict is VALID, the Lean proof is downloaded and handed to the local Lean kernel; the result tells you whether the proof is mathematically sound on your own machine, not Prova's. INVALID verdicts skip the kernel step and return the failure.

Returns: {certificate, kernel_check} — kernel_check is null when verdict != VALID or no proof was emitted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasoningYes
retainNo
source_urlNo
domainNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided. Description explains that the Lean proof is downloaded and run locally, and kernel_check is null for non-VALID verdicts. Discloses that result is on user's machine, not Prova's. Does not mention side effects but reasonable for a verification tool.

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

Conciseness4/5

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

Description is front-loaded with main action and uses clear structure. Bullet points for return values are helpful. Slightly verbose but still efficient. Could be more concise without losing clarity.

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

Completeness2/5

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

Given 4 parameters with 0% schema coverage and no param descriptions, the description is incomplete. It covers behavioral output well but leaves parameter semantics entirely unaddressed, which is critical for correct invocation.

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

Parameters1/5

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

Schema description coverage is 0%. Description provides no information about parameters (reasoning, retain, source_url, domain). Agent cannot infer their meaning or constraints beyond schema types.

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?

Description clearly states 'Verify reasoning, then locally kernel-check the emitted Lean proof.' and distinguishes from siblings by combining both steps in one call. Specific verb (verify, kernel-check) and resource (reasoning, Lean proof).

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?

Description says 'The complete trust loop in one call.' implying when to use. It explains that INVALID verdicts skip kernel step but does not explicitly mention when not to use or alternatives. Usage context is clear but lacks exclusions.

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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