Provides nine specialized production-ready solvers for advanced resource allocation, network flow, and multi-objective optimization with native Monte Carlo integration. It enables users to perform constraint-based decision-making and performance analysis directly through Claude Code.
Transforms unstructured natural language operational problems into rigorously formulated, validated, and solved Mixed-Integer Linear Programming (MILP) models via an autonomous multi-agent graph, bringing closed-loop Operations Research capabilities to LLM assistants and MCP clients.
Enables solving linear programming (LP) and mixed-integer linear programming (MILP) optimization problems through natural language, with built-in simplex and branch-and-cut solvers plus infeasibility diagnostics. Includes optional OR-Tools fallback for larger problems and supports parsing optimization problems from natural language descriptions.
A best-effort universal logic and numerical solver interface using MCP that implements the 'LLM sandwich' model to process queries, call dedicated solvers (ortools, cvxpy, z3), and verbalize results.
MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to:
Submit and validate constraint models
Set model parameters
Solve constraint satisfaction and optimization problems
Retrieve and analyze solution
Provides linear programming (LP), mixed-integer programming (MIP), and quadratic programming (QP) optimization capabilities using the HiGHS solver, enabling AI assistants to solve complex optimization problems like production planning, logistics, and portfolio optimization.
