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check_deep

Performs a deep faithfulness screen on informal-Lean statement pairs using two independent LLM judges and an adversarial counterexample probe to detect defects before shipping.

Instructions

Full faithfulness screen: everything in check_fast plus two independent LLM judges under strict consensus and an adversarial counterexample probe. Costs roughly 17–27¢ per statement (actual spend is reported in the response as actual_cost_usd) and takes 30–60 seconds — call it deliberately, before something ships. Requires ANTHROPIC_API_KEY.

This screen may only REJECT: an outcome of 'passed_screening' means no defect was found by this harness, NOT a certification of faithfulness (measured against human verdicts, human-rejected pairs still passed 17.0% of the time for theorems, 35.6% for definitions). Every flag — including a probe counterexample — is a candidate requiring human confirmation, not a verdict. The response ranks its evidence: a counterexample outranks a deterministic lint, which outranks two-judge consensus; a single-judge flag is below our own reporting bar.

kind is 'theorem' or 'definition'; omit it to infer from the Lean declaration head.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNo
leanYes
informalYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

The description discloses all relevant behavioral traits: cost range, time, required API key, the meaning of a 'passed' outcome (not a certification), false pass rates for theorems and definitions, and the evidence ranking. Since no annotations are provided, the description carries the full burden and fulfills it thoroughly.

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 appropriately sized for the complexity of the tool. It is front-loaded with the core purpose and then provides additional details. Every sentence adds value without redundancy, and it maintains a clear structure.

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 and that an output schema exists (but is not described here), the description covers most necessary aspects: behavior, cost, time, constraints, and interpretation of results. It could include more details on error conditions, but overall it is thorough for an AI agent to understand usage.

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

Parameters4/5

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

The description adds meaning for the 'kind' parameter by specifying it can be 'theorem' or 'definition' and if omitted it is inferred. For 'lean' and 'informal', the description does not elaborate beyond the tool's purpose, but given the context of faithfulness checking, their roles are deducible. Schema description coverage is 0%, so the description compensates partially.

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: a full faithfulness screen that includes everything in check_fast plus two independent LLM judges and an adversarial counterexample probe. It distinguishes itself from the sibling tool check_fast by specifying the additional components.

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

Usage Guidelines5/5

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

The description explicitly advises when to use this tool: 'call it deliberately, before something ships.' It contrasts with check_fast by noting the added thoroughness and cost, and warns about the time and expense (17-27¢, 30-60 seconds). This provides clear guidance on when to choose this tool over the sibling.

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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