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check_fast

Analyze informal↔Lean 4 pairs for lints, vacuity, triviality, and compilation errors. Flags issues for human review; a pass does not confirm faithfulness.

Instructions

Deterministic faithfulness screen for one informal↔Lean 4 pair: lints, vacuity/triviality checks, and (when a Lean+mathlib REPL is configured and warm) elaboration. Free — no API calls, no key — and ~0.1s once the REPL is warm, so call it constantly while drafting. Catches statements that do not compile, trivially satisfiable existentials, vacuously-true specs, and claims hidden behind an unused declaration.

This screen may only REJECT: an outcome of 'passed_screening' means no defect was found by this harness — it is NOT a certification of faithfulness (measured against human verdicts, human-rejected pairs still passed the full screen 17.0% of the time for theorems, 35.6% for definitions). Every flag is a candidate for human confirmation, not a verdict.

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?

Since no annotations are provided, the description fully details behavioral traits: deterministic, no API calls, ~0.1s speed, REPL dependency, and crucially, its limitations with false pass rates (17% and 35.6%). This level of disclosure is exceptional and helps the agent set correct expectations.

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 well-structured with three focused paragraphs: functionality/speed, limitations/false pass rates, and parameter details. Every sentence adds value, no redundancy, and key information is front-loaded. It wastes no space while being comprehensive.

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 existence of an output schema, the description appropriately omits return value details. It covers the tool's purpose, limitations, parameter semantics, and operational context. However, it lacks specificity on the exact input format for 'lean' (e.g., full file vs. snippet) and 'informal', which could be clarified for seamless use.

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?

With 0% schema description coverage, the description adds significant meaning: it explains that 'kind' is 'theorem' or 'definition' and can be omitted for inference, and it contextualizes 'informal' and 'lean' as a pair. It does not fully detail the expected format or constraints for the two required strings, but provides enough for basic use.

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 defines the tool as a deterministic faithfulness screen for informal-to-Lean 4 pairs, listing specific checks (lints, vacuity, triviality) and emphasizing its speed and cost-free nature. It distinguishes itself from the sibling 'check_deep' by implying a fast, shallow check suitable for frequent use during drafting.

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 provides strong usage context: 'call it constantly while drafting' and notes it is fast and free. However, it does not explicitly mention when to use the sibling 'check_deep' instead, leaving the comparison implicit rather than giving clear when-not guidance.

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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