Everything else takes a model and returns a solution — solve_lp through PuLP, solve_scipy_portfolio_optimization and solve_scipy_facility_location through SciPy, the colouring and N-Queens solvers as constraint problems — so the blast radius is a computation, with that single exception. The range runs from textbook set pieces (solve_sudoku, solve_24_point_game, solve_chicken_rabbit_problem) to models you would put in front of a real decision (solve_production_planning with sensitivity_analysis, solve_scipy_facility_location with fixed_cost and max_facilities), which makes run_example the quickest way to see which end a given solver sits at. Input shape is uneven across the list: solve_lp takes a single `problem` argument where the SciPy and planning solvers take the model broken into named parts.
A solver server over the gurddy package: linear and mixed-integer programs through PuLP, constraint problems such as N-Queens, Sudoku and graph or map colouring, minimax for games and for decisions under uncertainty, and SciPy-backed portfolio, fitting and facility-location models.
- Linear and mixed-integer programming: solve_lp takes a `problem` and solves it as an LP or a MIP through PuLP.
- A planning model with its sensitivity: solve_production_planning takes capacities, consumption and profits, an `integer` switch, and an optional sensitivity_analysis.
- Constraint problems solved directly rather than described: solve_n_queens takes `n`, solve_sudoku takes a 9x9 puzzle, solve_graph_coloring takes edges with num_vertices and max_colors, and solve_map_coloring takes regions with their adjacencies.
- Decisions against an adversary or against uncertainty: solve_minimax_game solves a two-player zero-sum game from a payoff_matrix for a given player, and solve_minimax_decision takes decision_vars, scenarios, an objective and a budget for robust optimisation.
- SciPy-backed numerical models: solve_scipy_portfolio_optimization takes expected_returns, a covariance_matrix and risk_tolerance; solve_scipy_statistical_fitting estimates parameters for a `distribution` from `data`; solve_scipy_facility_location combines CSP and SciPy over customer_locations, customer_demands, facility_locations, fixed_cost and max_facilities.
- Worked examples on demand: run_example runs one of lp, csp, n_queens, graph_coloring, map_coloring, scheduling, logic_puzzles, optimized_csp, optimized_lp, minimax, scipy_optimization or classic_problems.
- Word-problem shortcuts: solve_24_point_game finds an arithmetic expression over four numbers, and solve_chicken_rabbit_problem solves from total_heads and total_legs.
No credentials are configured. The solvers run on the gurddy package, which `install` is the tool for putting in place or upgrading, and `info` reports on.
One command — pip install gurddy_mcp