MCP-ORTools

A Model Context Protocol (MCP) server implementation using Google OR-Tools for constraint solving. Designed for use with Large Language Models through standardized constraint model specification.

Overview

MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the Model Context Protocol, enabling AI models to:

• Submit and validate constraint models

• Set model parameters

• Solve constraint satisfaction and optimization problems

• Retrieve and analyze solutions

Related MCP server: MCP Optimizer

Installation

1. Install the package:

pip install git+https://github.com/Jacck/mcp-ortools.git

2. Configure Claude Desktop Create the configuration file at %APPDATA%\Claude\claude_desktop_config.json (Windows) or ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):

{
  "mcpServers": {
    "ortools": {
      "command": "python",
      "args": ["-m", "mcp_ortools.server"]
    }
  }
}

Model Specification

Models are specified in JSON format with three main sections:

• variables: Define variables and their domains

• constraints: List of constraints using OR-Tools methods

• objective: Optional optimization objective

Constraint Syntax

Constraints must use OR-Tools method syntax:

• .__le__() for less than or equal (<=)

• .__ge__() for greater than or equal (>=)

• .__eq__() for equality (==)

• .__ne__() for not equal (!=)

Usage Examples

Simple Optimization Model

{
    "variables": [
        {"name": "x", "domain": [0, 10]},
        {"name": "y", "domain": [0, 10]}
    ],
    "constraints": [
        "(x + y).__le__(15)",
        "x.__ge__(2 * y)"
    ],
    "objective": {
        "expression": "40 * x + 100 * y",
        "maximize": true
    }
}

Knapsack Problem

Example: Select items with values [3,1,2,1] and weights [2,2,1,1] with total weight limit of 2.

{
    "variables": [
        {"name": "p0", "domain": [0, 1]},
        {"name": "p1", "domain": [0, 1]},
        {"name": "p2", "domain": [0, 1]},
        {"name": "p3", "domain": [0, 1]}
    ],
    "constraints": [
        "(2*p0 + 2*p1 + p2 + p3).__le__(2)"
    ],
    "objective": {
        "expression": "3*p0 + p1 + 2*p2 + p3",
        "maximize": true
    }
}

Additional constraints example:

{
    "constraints": [
        "p0.__eq__(1)",         // Item p0 must be selected
        "p1.__ne__(p2)",        // Can't select both p1 and p2
        "(p2 + p3).__ge__(1)"   // Must select at least one of p2 or p3
    ]
}

Features

• Full OR-Tools CP-SAT solver support

• JSON-based model specification

• Support for:

  • Integer and boolean variables (domain: [min, max])

  • Linear constraints using OR-Tools method syntax

  • Linear optimization objectives

  • Timeouts and solver parameters

  • Binary constraints and relationships

  • Portfolio selection problems

  • Knapsack problems

Supported Operations in Constraints

• Basic arithmetic: +, -, *

• Comparisons: .le(), .ge(), .eq(), .ne()

• Linear combinations of variables

• Binary logic through combinations of constraints

Development

To setup for development:

git clone https://github.com/Jacck/mcp-ortools.git
cd mcp-ortools
pip install -e .

Model Response Format

The solver returns solutions in JSON format:

{
    "status": "OPTIMAL",
    "solve_time": 0.045,
    "variables": {
        "p0": 0,
        "p1": 0,
        "p2": 1,
        "p3": 1
    },
    "objective_value": 3.0
}

Status values:

• OPTIMAL: Found optimal solution

• FEASIBLE: Found feasible solution

• INFEASIBLE: No solution exists

• UNKNOWN: Could not determine solution

License

MIT License - see LICENSE file for details