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.
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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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