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Servers/Code Execution/gurddy mcp
Code ExecutionOfficial
G

gurddy mcp

his repository contains a fully functional MCP (Model Context Protocol) server, providing solutions for Constraint Satisfaction Problems (CSP) and Linear Programming (LP). It is based on the gurddy package and supports solving a variety of classic problems.

2 starsUpdated Oct 24, 2025
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Overview

A comprehensive Model Context Protocol (MCP) server for solving Constraint Satisfaction Problems (CSP), Linear Programming (LP), Minimax optimization, and SciPy-powered advanced optimization problems. Built on the gurddy optimization library with SciPy integration, it supports solving various classic problems through two MCP transports: stdio (for IDE integration) and streamable HTTP (for web clients). 🚀 Quick Start (Stdio): pip install gurddy_mcp then configure in your IDE 🌐 Quick Start (HTTP): docker run -p 8080:8080 gurddy-mcp or see deployment guide

Installation

pip install gurddy_mcp

Configuration

{
  "mcpServers": {
    "gurddy": {
      "command": "uvx",
      "args": ["gurddy-mcp@latest"],
      "env": {},
      "disabled": false,
      "autoApprove": [
        "run_example",
        "info",
        "install",
        "solve_n_queens",
        "solve_sudoku",
        "solve_graph_coloring",
        "solve_map_coloring",
        "solve_lp",
        "solve_production_planning",
        "solve_minimax_game",
        "solve_minimax_decision",
        "solve_24_point_game",
        "solve_chicken_rabbit_problem",
        "solve_scipy_portfolio_optimization",
        "solve_scipy_statistical_fitting",
        "solve_scipy_facility_location"
      ]
    }
  }

Capabilities

  • Solve N-Queens for any board size

    Ask your AI to place N queens on an N×N board with no attacks using `solve_n_queens`.

  • Solve Sudoku puzzles

    Provide a 9×9 grid with zeros for blanks and get the completed solution via `solve_sudoku`.

  • Optimize production plans

    Maximize profit across products given resource constraints and optional sensitivity analysis with `solve_production_planning`.

  • Find Nash equilibria in zero-sum games

    Compute optimal mixed strategies for two-player games like Rock-Paper-Scissors using `solve_minimax_game`.

  • Solve the 24-Point game

    Find arithmetic expressions that evaluate to 24 from four given numbers with `solve_24_point_game`.

  • Optimize investment portfolios with SciPy

    Maximize risk-adjusted returns using mean-variance optimization via `solve_scipy_portfolio_optimization`.

Tags

  • project-management
  • workflow-automation
  • development

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