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MCP-Logic: AI Logic Acceleration & Seamless Integration - MCP Implementation

MCP-Logic: AI Logic Acceleration & Seamless Integration

MCP-Logic: Boost your AI logic tasks with Prover9/Mace4-powered calculations via MCP-Server. Effortless integration for Windows, Claude App & more! Smarter logic, faster results.

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Ranked in the top 9% of all AI tools in its category

About MCP-Logic

What is MCP-Logic: AI Logic Acceleration & Seamless Integration?

MCP-Logic is a specialized framework designed to accelerate formal logic processing in AI systems. It integrates Prover9/Mace4, a powerful automated theorem prover, with modern tooling to enable rigorous validation of logical statements and proofs. The system bridges the gap between academic logic frameworks and practical AI development, offering API-driven access to automated reasoning capabilities.

Key components include:

  • Prover9 integration for theorem proving
  • Custom tooling for logic validation and proof generation
  • Modular configuration for AI system integration

How to Use MCP-Logic: AI Logic Acceleration & Seamless Integration?

Setup Process

  1. Install prerequisites: Python 3.12+ and UV package manager
  2. Download certified Prover9 binaries from official sources
  3. Configure environment with precise toolchain paths
  4. Initialize via uv-based package management system

API Workflow

Use standardized JSON requests to:

  • Submit proof queries with premises/conclusions
  • Validate logical expressions syntax
  • Execute automated theorem proving pipelines

MCP-Logic Features

Key Features of MCP-Logic: AI Logic Acceleration & Seamless Integration?

Automated Reasoning Engine

Direct access to Prover9's advanced resolution/refutation algorithms

API-First Design

RESTful interfaces for seamless integration into ML/AI pipelines

Configurable Workflows

Customizable logic processing chains via JSON schema configurations

Validation Toolkit

Includes syntax/semantic checkers for logical expressions

Use Cases of MCP-Logic: AI Logic Acceleration & Seamless Integration?

  • AI System Validation: Prove correctness of decision-making algorithms
  • Research Prototyping: Rapid testing of logical hypotheses
  • Education Tools: Interactive proof assistants for logic courses
  • Security Verification: Formal validation of cryptographic protocols

MCP-Logic FAQ

Frequently Asked Questions

Q: What dependencies are required?

A: Python 3.12+, UV package manager, and native Prover9 binaries

Q: Can this be used in production systems?

A: Yes, with proper configuration and performance tuning

Q: How are proofs formatted?

A: Output follows standardized JSON-LD schema with proof steps

Q: Are there community resources?

A: Official docs and example notebooks provided on GitHub

Content

MCP-Logic

An MCP server providing automated reasoning capabilities using Prover9/Mace4 for AI systems. This server enables logical theorem proving and logical model verification through a clean MCP interface.

Design Philosophy

MCP-Logic bridges the gap between AI systems and formal logic by providing a robust interface to Prover9/Mace4. What makes it special:

  • AI-First Design : Built specifically for AI systems to perform automated reasoning
  • Knowledge Validation : Enables formal verification of knowledge representations and logical implications
  • Clean Integration : Seamless integration with the Model Context Protocol (MCP) ecosystem
  • Deep Reasoning : Support for complex logical proofs with nested quantifiers and multiple premises
  • Real-World Applications : Particularly useful for validating AI knowledge models and reasoning chains

Features

  • Seamless integration with Prover9 for automated theorem proving
  • Support for complex logical formulas and proofs
  • Built-in syntax validation
  • Clean MCP server interface
  • Extensive error handling and logging
  • Support for knowledge representation and reasoning about AI systems

Quick Example

image

# Prove that understanding + context leads to application
result = await prove(
    premises=[
        "all x all y (understands(x,y) -> can_explain(x,y))",
        "all x all y (can_explain(x,y) -> knows(x,y))",
        "all x all y (knows(x,y) -> believes(x,y))",
        "all x all y (believes(x,y) -> can_reason_about(x,y))",
        "all x all y (can_reason_about(x,y) & knows_context(x,y) -> can_apply(x,y))",
        "understands(system,domain)",
        "knows_context(system,domain)"
    ],
    conclusion="can_apply(system,domain)"
)
# Returns successful proof!

{'result': 'proved', 'proof': '', 'complete_output': '============================== Prover9 ===============================\nProver9 (32) version Dec-2007, Dec 2007.\nProcess 27928 was started by angry on Ty,\nMon Jan 13 16:10:57 2025\nThe command was "/cygdrive/f/Prover9-Mace4/bin-win32/prover9 -f C:\Users\angry\AppData\Local\Temp\tmp05_k_2ak.in".\n============================== end of head ===========================\n\n============================== INPUT =================================\n\n% Reading from file C:\Users\angry\AppData\Local\Temp\tmp05_k_2ak.in\n\n\nformulas(assumptions).\n(all x all y (understands(x,y) -> can_explain(x,y))).\n(all x all y (can_explain(x,y) -> knows(x,y))).\n(all x all y (knows(x,y) -> believes(x,y))).\n(all x all y (believes(x,y) -> can_reason_about(x,y))).\n(all x all y (can_reason_about(x,y) & knows_context(x,y) -> can_apply(x,y))).\nunderstands(system,domain).\nknows_context(system,domain).\nend_of_list.\n\nformulas(goals).\ncan_apply(system,domain).\nend_of_list.\n\n============================== end of input ==========================\n\n============================== PROCESS NON-CLAUSAL FORMULAS ==========\n\n% Formulas that are not ordinary clauses:\n1 (all x all y (understands(x,y) -> can_explain(x,y))) # label(non_clause). [assumption].\n2 (all x all y (can_explain(x,y) -> knows(x,y))) # label(non_clause). [assumption].\n3 (all x all y (knows(x,y) -> believes(x,y))) # label(non_clause). [assumption].\n4 (all x all y (believes(x,y) -> can_reason_about(x,y))) # label(non_clause). [assumption].\n5 (all x all y (can_reason_about(x,y) & knows_context(x,y) -> can_apply(x,y))) # label(non_clause). [assumption].\n6 can_apply(system,domain) # label(non_clause) # label(goal). [goal].\n\n============================== end of process non-clausal formulas ===\n\n============================== PROCESS INITIAL CLAUSES ===============\n\n% Clauses before input processing:\n\nformulas(usable).\nend_of_list.\n\nformulas(sos).\n-understands(x,y) | can_explain(x,y). [clausify(1)].\n-can_explain(x,y) | knows(x,y). [clausify(2)].\n-knows(x,y) | believes(x,y). [clausify(3)].\n-believes(x,y) | can_reason_about(x,y). [clausify(4)].\n-can_reason_about(x,y) | -knows_context(x,y) | can_apply(x,y). [clausify(5)].\nunderstands(system,domain). [assumption].\nknows_context(system,domain). [assumption].\n-can_apply(system,domain). [deny(6)].\nend_of_list.\n\nformulas(demodulators).\nend_of_list.\n\n============================== PREDICATE ELIMINATION =================\n\nEliminating understands/2\n7 understands(system,domain). [assumption].\n8 -understands(x,y) | can_explain(x,y). [clausify(1)].\nDerived: can_explain(system,domain). [resolve(7,a,8,a)].\n\nEliminating can_explain/2\n9 can_explain(system,domain). [resolve(7,a,8,a)].\n10 -can_explain(x,y) | knows(x,y). [clausify(2)].\nDerived: knows(system,domain). [resolve(9,a,10,a)].\n\nEliminating knows/2\n11 knows(system,domain). [resolve(9,a,10,a)].\n12 -knows(x,y) | believes(x,y). [clausify(3)].\nDerived: believes(system,domain). [resolve(11,a,12,a)].\n\nEliminating believes/2\n13 believes(system,domain). [resolve(11,a,12,a)].\n14 -believes(x,y) | can_reason_about(x,y). [clausify(4)].\nDerived: can_reason_about(system,domain). [resolve(13,a,14,a)].\n\nEliminating can_reason_about/2\n15 can_reason_about(system,domain). [resolve(13,a,14,a)].\n16 -can_reason_about(x,y) | -knows_context(x,y) | can_apply(x,y). [clausify(5)].\nDerived: -knows_context(system,domain) | can_apply(system,domain). [resolve(15,a,16,a)].\n\nEliminating knows_context/2\n17 -knows_context(system,domain) | can_apply(system,domain). [resolve(15,a,16,a)].\n18 knows_context(system,domain). [assumption].\nDerived: can_apply(system,domain). [resolve(17,a,18,a)].\n\nEliminating can_apply/2\n19 can_apply(system,domain). [resolve(17,a,18,a)].\n20 -can_apply(system,domain). [deny(6)].\nDerived: $F. [resolve(19,a,20,a)].\n\n============================== end predicate elimination =============\n\nAuto_denials: (no changes).\n\nTerm ordering decisions:\nPredicate symbol precedence: predicate_order([ ]).\nFunction symbol precedence: function_order([ ]).\nAfter inverse_order: (no changes).\nUnfolding symbols: (none).\n\nAuto_inference settings:\n % set(neg_binary_resolution). % (HNE depth_diff=0)\n % clear(ordered_res). % (HNE depth_diff=0)\n % set(ur_resolution). % (HNE depth_diff=0)\n % set(ur_resolution) -> set(pos_ur_resolution).\n % set(ur_resolution) -> set(neg_ur_resolution).\n\nAuto_process settings: (no changes).\n\n============================== PROOF =================================\n\n% Proof 1 at 0.00 (+ 0.00) seconds.\n% Length of proof is 21.\n% Level of proof is 8.\n% Maximum clause weight is 0.\n% Given clauses 0.\n\n1 (all x all y (understands(x,y) -> can_explain(x,y))) # label(non_clause). [assumption].\n2 (all x all y (can_explain(x,y) -> knows(x,y))) # label(non_clause). [assumption].\n3 (all x all y (knows(x,y) -> believes(x,y))) # label(non_clause). [assumption].\n4 (all x all y (believes(x,y) -> can_reason_about(x,y))) # label(non_clause). [assumption].\n5 (all x all y (can_reason_about(x,y) & knows_context(x,y) -> can_apply(x,y))) # label(non_clause). [assumption].\n6 can_apply(system,domain) # label(non_clause) # label(goal). [goal].\n7 understands(system,domain). [assumption].\n8 -understands(x,y) | can_explain(x,y). [clausify(1)].\n9 can_explain(system,domain). [resolve(7,a,8,a)].\n10 -can_explain(x,y) | knows(x,y). [clausify(2)].\n11 knows(system,domain). [resolve(9,a,10,a)].\n12 -knows(x,y) | believes(x,y). [clausify(3)].\n13 believes(system,domain). [resolve(11,a,12,a)].\n14 -believes(x,y) | can_reason_about(x,y). [clausify(4)].\n15 can_reason_about(system,domain). [resolve(13,a,14,a)].\n16 -can_reason_about(x,y) | -knows_context(x,y) | can_apply(x,y). [clausify(5)].\n17 -knows_context(system,domain) | can_apply(system,domain). [resolve(15,a,16,a)].\n18 knows_context(system,domain). [assumption].\n19 can_apply(system,domain). [resolve(17,a,18,a)].\n20 -can_apply(system,domain). [deny(6)].\n21 $F. [resolve(19,a,20,a)].\n\n============================== end of proof ==========================\n\n============================== STATISTICS ============================\n\nGiven=0. Generated=1. Kept=0. proofs=1.\nUsable=0. Sos=0. Demods=0. Limbo=0, Disabled=15. Hints=0.\nWeight_deleted=0. Literals_deleted=0.\nForward_subsumed=0. Back_subsumed=0.\nSos_limit_deleted=0. Sos_displaced=0. Sos_removed=0.\nNew_demodulators=0 (0 lex), Back_demodulated=0. Back_unit_deleted=0.\nDemod_attempts=0. Demod_rewrites=0.\nRes_instance_prunes=0. Para_instance_prunes=0. Basic_paramod_prunes=0.\nNonunit_fsub_feature_tests=0. Nonunit_bsub_feature_tests=0.\nMegabytes=0.02.\nUser_CPU=0.00, System_CPU=0.00, Wall_clock=0.\n\n============================== end of statistics =====================\n\n============================== end of search =========================\n\nTHEOREM PROVED\n\nExiting with 1 proof.\n\nProcess 27928 exit (max_proofs) Mon Jan 13 16:10:57 2025\n'}

image

Installation

Prerequisites

Setup

  1. Install Prover9/Mace4 to a known location (e.g., F:/Prover9-Mace4/)
  2. Clone this repository
  3. Install dependencies:
uv venv
uv pip install -e .

Configuration

Add to your MCP environment configuration:

{
  "mcpServers": {
    "mcp-logic": {
      "command": "uv",
      "args": [
        "--directory",
        "path/to/mcp-logic/src/mcp_logic",
        "run",
        "mcp_logic",
        "--prover-path",
        "path/to/Prover9-Mace4/bin-win32"
      ]
    }
  }
}

Available Tools

prove

Run logical proofs using Prover9:

{
  "tool": "prove",
  "arguments": {
    "premises": [
      "all x (man(x) -> mortal(x))",
      "man(socrates)"
    ],
    "conclusion": "mortal(socrates)"
  }
}

check-well-formed

Validate logical statement syntax:

{
  "tool": "check-well-formed",
  "arguments": {
    "statements": [
      "all x (man(x) -> mortal(x))",
      "man(socrates)"
    ]
  }
}

Documentation

See the Documents folder for detailed analysis and examples:

Project Structure

mcp-logic/
├── src/
│   └── mcp_logic/
│       └── server.py    # Main MCP server implementation
├── tests/
│   ├── test_proofs.py   # Core functionality tests
│   └── test_debug.py    # Debug utilities
├── Documents/           # Analysis and documentation
├── pyproject.toml      # Python package config
└── README.md          # This file

Development

Run tests:

uv pip install pytest
uv run pytest

License

MIT

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