Gurddy MCP Server

A comprehensive Model Context Protocol (MCP) server for solving Constraint Satisfaction Problems (CSP), Linear Programming (LP), Minimax optimization, and

Recorded tools

• info — Get information about the gurddy package. json { "name": "info", "arguments": {} }
• run_example — Run a gurddy example. json { "name": "run_example", "arguments": { "example": "n_queens" } } Available examples: lp, csp, n_queens, graph_coloring, map_coloring, scheduling, logic_puzzles, optimized_csp, optimized_lp, mi
• solve_n_queens — Solve the N-Queens problem. json { "name": "solve_n_queens", "arguments": { "n": 8 } }
• solve_sudoku — Solve a 9x9 Sudoku puzzle. json { "name": "solve_sudoku", "arguments": { "puzzle": [[5,3,0,...], [6,0,0,...], ...] } }
• solve_graph_coloring — Solve graph coloring problem. json { "name": "solve_graph_coloring", "arguments": { "edges": [[0,1], [1,2], [2,0]], "num_vertices": 3, "max_colors": 3 } }
• solve_map_coloring — Solve map coloring problem. json { "name": "solve_map_coloring", "arguments": { "regions": ["A", "B", "C"], "adjacencies": [["A", "B"], ["B", "C"]], "max_colors": 2 } }
• solve_lp — Solve a Linear Programming (LP) or Mixed Integer Programming (MIP) problem using PuLP. json { "name": "solve_lp", "arguments": { "profits": { "ProductA": 30, "ProductB": 40 }, "consumption": { "ProductA": {"Labor": 2, "M
• solve_production_planning — Solve a production planning optimization problem with optional sensitivity analysis. json { "name": "solve_production_planning", "arguments": { "profits": { "ProductA": 30, "ProductB": 40 }, "consumption": { "ProductA":
• solve_minimax_game — Solve a two-player zero-sum game using minimax (game theory). json { "name": "solve_minimax_game", "arguments": { "payoff_matrix": [ [0, -1, 1], [1, 0, -1], [-1, 1, 0] ], "player": "row" } } Returns the optimal mixed str
• solve_minimax_decision — Solve a minimax decision problem under uncertainty (robust optimization). json { "name": "solve_minimax_decision", "arguments": { "scenarios": [ {"A": -0.2, "B": -0.1, "C": 0.05}, {"A": 0.3, "B": 0.2, "C": -0.02}, {"A":
• solve_24_point_game — Solve the 24-point game with four numbers using arithmetic operations. json { "name": "solve_24_point_game", "arguments": { "numbers": [1, 2, 3, 4] } } Finds arithmetic expressions using +, -, *, / and parentheses to rea
• solve_chicken_rabbit_problem — Solve the classic chicken-rabbit problem with heads and legs constraints. json { "name": "solve_chicken_rabbit_problem", "arguments": { "total_heads": 35, "total_legs": 94 } } Determines the number of chickens (2 legs) a
• solve_scipy_portfolio_optimization — Solve nonlinear portfolio optimization using SciPy with quadratic risk models. json { "name": "solve_scipy_portfolio_optimization", "arguments": { "expected_returns": [0.12, 0.18, 0.15], "covariance_matrix": [ [0.04, 0.0
• solve_scipy_statistical_fitting — Solve statistical parameter estimation using SciPy with distribution fitting. json { "name": "solve_scipy_statistical_fitting", "arguments": { "data": [1.2, 2.3, 1.8, 2.1, 1.9, 2.4, 1.7, 2.0], "distribution": "normal" }

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