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Verify logical conclusions from premises using resolution-based theorem proving. Input premises and conclusion to determine if the statement follows logically.

Instructions

Prove a logical statement using resolution.

When to use: You have premises and want to verify a conclusion follows logically. When NOT to use: You want to find counterexamples (use find-counterexample instead).

Example: premises: ["all x (man(x) -> mortal(x))", "man(socrates)"] conclusion: "mortal(socrates)" → Returns: { success: true, result: "proved" }

Common issues:

  • "No proof found" often means inference limit reached, not that the theorem is false

  • Try increasing inference_limit for complex proofs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
premisesYesList of logical premises in FOL syntax
conclusionYesStatement to prove
inference_limitNoMax inference steps before giving up (default: 1000). Increase for complex proofs.
enable_arithmeticNoEnable arithmetic predicates (lt, gt, plus, minus, times, etc.). Default: false.
enable_equalityNoAuto-inject equality axioms (reflexivity, symmetry, transitivity, congruence). Default: false.
engineNoReasoning engine: 'prolog' (Horn clauses), 'sat' (general FOL), 'auto' (select based on formula). Default: 'auto'.
verbosityNoResponse verbosity: 'minimal' (token-efficient), 'standard' (default), 'detailed' (debug info)
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the tool performs logical proof via resolution, explains that 'No proof found' often means inference limit reached (not theorem false), and suggests increasing inference_limit for complex proofs. It also provides an example of the return format. While comprehensive, it could mention more about error handling or performance characteristics.

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 clear sections (purpose, usage guidelines, example, common issues), each sentence adds value, and it's front-loaded with the core purpose. There's no redundant or wasted text, making it highly efficient for an AI agent to parse.

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 (7 parameters, logical reasoning), no annotations, and no output schema, the description does a strong job. It explains the tool's purpose, usage context, provides an example output, and addresses common pitfalls. However, without an output schema, it could more explicitly detail the full range of possible return values or error conditions beyond the example.

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?

Schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds minimal parameter-specific information beyond the schema (e.g., it mentions inference_limit in the 'Common issues' section). This meets the baseline of 3 where the schema does the heavy lifting, but the description doesn't significantly enhance parameter understanding.

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 explicitly states the tool's purpose: 'Prove a logical statement using resolution.' It specifies the verb ('prove'), resource ('logical statement'), and method ('using resolution'), clearly distinguishing it from siblings like 'find-counterexample' or 'find-model' which serve different logical functions.

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 includes dedicated 'When to use' and 'When NOT to use' sections, explicitly stating to use this tool for verifying conclusions from premises and to use 'find-counterexample' instead for finding counterexamples. This provides clear, actionable guidance on tool selection versus alternatives.

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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