📌Overview

Purpose: MCP-ORTools serves as an implementation of the Model Context Protocol using Google OR-Tools for constraint solving, aimed at facilitating interaction with Large Language Models.

Overview: MCP-ORTools integrates constraint programming capabilities of Google OR-Tools with AI models, allowing users to easily define, solve, and analyze constraints through standardized JSON specifications.

Key Features:

Full OR-Tools CP-SAT Solver Support: Enables users to leverage advanced constraint solving techniques for various optimization problems.
JSON-based Model Specification: Simplifies model creation and readability by using a structured JSON format that outlines variables, constraints, and objectives.
Support for Various Variable Types: Handles integer and boolean variables with defined domains, making it versatile for different modeling scenarios.
Comprehensive Constraints and Objectives: Utilizes OR-Tools method syntax for defining linear constraints and allows for the maximization or minimization of optimization objectives, supporting a wide range of problems including portfolio selection and knapsack problems.
Solver Parameters and Timeouts: Offers configuration options for time management and solver behavior to optimize performance.

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

Installation

Install the package:

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

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

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)",
        "p1.__ne__(p2)",
        "(p2 + p3).__ge__(1)"
    ]
}

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 set up 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