MCP OR-Tools Server

Example Use Cases

• 1

  Construct, validate, and solve constraint satisfaction and optimization models using OR-Tools via a standardized MCP interface.

• 2

  Deterministic JSON-formatted solutions (variable assignments, status, objective value, and solve time) for linear and boolean constraint problems.

• 3

  Integrate combinatorial solvers into LLM workflows for tasks like knapsack selection, portfolio selection, and simple scheduling under constraints.

Description

This project integrates Google's OR-Tools CP-SAT solver with the Model Context Protocol, allowing models described in JSON to be executed and optimized programmatically. It supports integer and boolean variables, linear constraints using OR-Tools method syntax, and linear objectives, plus solver parameters like timeouts. The server returns structured JSON solutions (status, variable assignments, objective value, solve time), making it easy to embed constraint solving into LLM-driven workflows. It's useful for problems such as knapsack selection, portfolio choice, scheduling, and other combinatorial optimization tasks.