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deterministic-logic

by SamSoupSauce

Deterministic Logic Evaluation MCP Toolkit (deterministic-logic-mcp)

A Model Context Protocol (MCP) server providing high-performance, deterministic logic evaluation tools for AI assistants and automated systems.

Features & Logic Engines

  1. Propositional Logic & AST Evaluator: Parse and evaluate boolean expressions with full variable mapping. Supports &&, ||, !, ^ (XOR), => (IMPLIES), <=> (IFF), and custom operator notation.

  2. Truth Table Generator: Compute complete $2^N$ truth tables, check tautologies, contradictions, and satisfiability.

  3. DPLL SAT Solver: Convert formulas to Conjunctive Normal Form (CNF) and find satisfying variable assignments or prove UNSAT.

  4. JSON Logic Rule Engine: Deterministic evaluation of structured rules (boolean, arithmetic, comparison, array filters/maps, conditional branching) against JSON datasets.

  5. Decision Table Engine: Grid-based rule evaluation with support for wildcards, first_match, all_matches, and strict_single_match (determinism verification).

  6. State Machine Analyzer & Verifier: Check Finite State Machines (FSMs) for determinism, deadlocks, unreachable states, and state reachability path extraction.


Related MCP server: Sys8 MCP Server

🛠️ MCP Tools Reference

Tool Name

Description

Key Arguments

evaluate_boolean

Evaluates a boolean logic expression with variable values

expression, env

generate_truth_table

Generates full truth table & computes Tautology / Contradiction

expression, maxVariables

solve_sat

DPLL SAT solver returning satisfying variable assignment or UNSAT

expression

evaluate_json_logic

Evaluates JSON logic rules against context JSON data

rule, data

evaluate_decision_table

Evaluates matrix decision table rules with determinism checks

rows, inputs, mode

analyze_state_machine

Checks FSM for determinism, deadlocks, and unreachable states

initialState, transitions, terminalStates

verify_state_reachability

Finds shortest execution path to target state in FSM

initialState, transitions, targetState

simulate_state_machine

Simulates an input sequence through an FSM step-by-step

initialState, transitions, inputSequence


🚀 Quickstart & Setup

Building Locally

npm install
npm run build
npm test

Configuring in MCP Clients (e.g. Claude Desktop, Cursor, AGY)

Add the following to your mcpServers configuration file (e.g. claude_desktop_config.json):

{
  "mcpServers": {
    "deterministic-logic": {
      "command": "node",
      "args": ["/home/mrovkill/Projects/deterministic-logic/dist/index.js"]
    }
  }
}

Or run directly via npx / tsx:

{
  "mcpServers": {
    "deterministic-logic": {
      "command": "npx",
      "args": ["tsx", "/home/mrovkill/Projects/deterministic-logic/src/index.ts"]
    }
  }
}

đź’ˇ Example Tool Usage

1. Truth Table Generation

Input: expression = "A => (B => A)"
Output: isTautology = true, isSatisfiable = true, truthTable rows showing all evaluated combinations.

2. DPLL SAT Solving

Input: expression = "(A || B) && (!A || B) && (!B)"
Output: satisfiable = false (UNSAT).

3. JSON Logic Rule Execution

Input:

{
  "rule": {
    "and": [
      { ">": [{ "var": "user.age" }, 18] },
      { "in": [{ "var": "user.role" }, ["admin", "editor"]] }
    ]
  },
  "data": { "user": { "age": 25, "role": "admin" } }
}

Output: result = true.


đź“„ License

MIT

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